diff --git a/docs/introduction-to-scikit-learn.ipynb b/docs/introduction-to-scikit-learn.ipynb index 06e42ee16..dd8f935ab 100644 --- a/docs/introduction-to-scikit-learn.ipynb +++ b/docs/introduction-to-scikit-learn.ipynb @@ -15,14 +15,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Last updated: 2023-10-06 09:51:53.957505\n" + "Last updated: 2023-10-13 09:47:50.863667\n" ] } ], @@ -134,14 +134,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Using Scikit-Learn version: 1.3.0 (materials in this notebook require this version or newer).\n" + "Using Scikit-Learn version: 1.3.1 (materials in this notebook require this version or newer).\n" ] } ], @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -364,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -522,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -569,7 +569,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -613,7 +613,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -632,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -694,7 +694,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -732,14 +732,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/daniel/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:464: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", + "/Users/daniel/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:465: UserWarning: X does not have valid feature names, but RandomForestClassifier was fitted with feature names\n", " warnings.warn(\n" ] }, @@ -750,12 +750,12 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# This doesn't work... incorrect shapes\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m y_label \u001b[38;5;241m=\u001b[39m \u001b[43mclf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:823\u001b[0m, in \u001b[0;36mForestClassifier.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[1;32m 803\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 804\u001b[0m \u001b[38;5;124;03m Predict class for X.\u001b[39;00m\n\u001b[1;32m 805\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[38;5;124;03m The predicted classes.\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 823\u001b[0m proba \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict_proba\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_outputs_ \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 826\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclasses_\u001b[38;5;241m.\u001b[39mtake(np\u001b[38;5;241m.\u001b[39margmax(proba, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m), axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:865\u001b[0m, in \u001b[0;36mForestClassifier.predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 863\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 864\u001b[0m \u001b[38;5;66;03m# Check data\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_X_predict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 867\u001b[0m \u001b[38;5;66;03m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[1;32m 868\u001b[0m n_jobs, _, _ \u001b[38;5;241m=\u001b[39m _partition_estimators(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_estimators, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_jobs)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:599\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[38;5;124;03mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[1;32m 598\u001b[0m check_is_fitted(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 599\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsr\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 600\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(X) \u001b[38;5;129;01mand\u001b[39;00m (X\u001b[38;5;241m.\u001b[39mindices\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc \u001b[38;5;129;01mor\u001b[39;00m X\u001b[38;5;241m.\u001b[39mindptr\u001b[38;5;241m.\u001b[39mdtype \u001b[38;5;241m!=\u001b[39m np\u001b[38;5;241m.\u001b[39mintc):\n\u001b[1;32m 601\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:604\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m 602\u001b[0m out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m 603\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 604\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_y:\n\u001b[1;32m 606\u001b[0m out \u001b[38;5;241m=\u001b[39m _check_y(y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_params)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:940\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 938\u001b[0m \u001b[38;5;66;03m# If input is 1D raise error\u001b[39;00m\n\u001b[1;32m 939\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m array\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m--> 940\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 941\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExpected 2D array, got 1D array instead:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124marray=\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 942\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 943\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 944\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mif it contains a single sample.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(array)\n\u001b[1;32m 945\u001b[0m )\n\u001b[1;32m 947\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype_numeric \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(array\u001b[38;5;241m.\u001b[39mdtype, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mkind\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m array\u001b[38;5;241m.\u001b[39mdtype\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUSV\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 948\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 949\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdtype=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 950\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 951\u001b[0m )\n", + "\u001b[1;32m/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb Cell 24\u001b[0m line \u001b[0;36m2\n\u001b[1;32m 1\u001b[0m \u001b[39m# This doesn't work... incorrect shapes\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m y_label \u001b[39m=\u001b[39m clf\u001b[39m.\u001b[39;49mpredict(np\u001b[39m.\u001b[39;49marray([\u001b[39m0\u001b[39;49m, \u001b[39m2\u001b[39;49m, \u001b[39m3\u001b[39;49m, \u001b[39m4\u001b[39;49m]))\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:823\u001b[0m, in \u001b[0;36mForestClassifier.predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mpredict\u001b[39m(\u001b[39mself\u001b[39m, X):\n\u001b[1;32m 803\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 804\u001b[0m \u001b[39m Predict class for X.\u001b[39;00m\n\u001b[1;32m 805\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 821\u001b[0m \u001b[39m The predicted classes.\u001b[39;00m\n\u001b[1;32m 822\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[0;32m--> 823\u001b[0m proba \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpredict_proba(X)\n\u001b[1;32m 825\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_outputs_ \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[1;32m 826\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclasses_\u001b[39m.\u001b[39mtake(np\u001b[39m.\u001b[39margmax(proba, axis\u001b[39m=\u001b[39m\u001b[39m1\u001b[39m), axis\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:865\u001b[0m, in \u001b[0;36mForestClassifier.predict_proba\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 863\u001b[0m check_is_fitted(\u001b[39mself\u001b[39m)\n\u001b[1;32m 864\u001b[0m \u001b[39m# Check data\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_X_predict(X)\n\u001b[1;32m 867\u001b[0m \u001b[39m# Assign chunk of trees to jobs\u001b[39;00m\n\u001b[1;32m 868\u001b[0m n_jobs, _, _ \u001b[39m=\u001b[39m _partition_estimators(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_estimators, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mn_jobs)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:599\u001b[0m, in \u001b[0;36mBaseForest._validate_X_predict\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m 596\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 597\u001b[0m \u001b[39mValidate X whenever one tries to predict, apply, predict_proba.\"\"\"\u001b[39;00m\n\u001b[1;32m 598\u001b[0m check_is_fitted(\u001b[39mself\u001b[39m)\n\u001b[0;32m--> 599\u001b[0m X \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(X, dtype\u001b[39m=\u001b[39;49mDTYPE, accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsr\u001b[39;49m\u001b[39m\"\u001b[39;49m, reset\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[1;32m 600\u001b[0m \u001b[39mif\u001b[39;00m issparse(X) \u001b[39mand\u001b[39;00m (X\u001b[39m.\u001b[39mindices\u001b[39m.\u001b[39mdtype \u001b[39m!=\u001b[39m np\u001b[39m.\u001b[39mintc \u001b[39mor\u001b[39;00m X\u001b[39m.\u001b[39mindptr\u001b[39m.\u001b[39mdtype \u001b[39m!=\u001b[39m np\u001b[39m.\u001b[39mintc):\n\u001b[1;32m 601\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mNo support for np.int64 index based sparse matrices\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:605\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m 603\u001b[0m out \u001b[39m=\u001b[39m X, y\n\u001b[1;32m 604\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m no_val_y:\n\u001b[0;32m--> 605\u001b[0m out \u001b[39m=\u001b[39m check_array(X, input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m 606\u001b[0m \u001b[39melif\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_y:\n\u001b[1;32m 607\u001b[0m out \u001b[39m=\u001b[39m _check_y(y, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_params)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:938\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[39m# If input is 1D raise error\u001b[39;00m\n\u001b[1;32m 937\u001b[0m \u001b[39mif\u001b[39;00m array\u001b[39m.\u001b[39mndim \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m--> 938\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 939\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mExpected 2D array, got 1D array instead:\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39marray=\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[1;32m 940\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mReshape your data either using array.reshape(-1, 1) if \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 941\u001b[0m \u001b[39m\"\u001b[39m\u001b[39myour data has a single feature or array.reshape(1, -1) \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 942\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mif it contains a single sample.\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(array)\n\u001b[1;32m 943\u001b[0m )\n\u001b[1;32m 945\u001b[0m \u001b[39mif\u001b[39;00m dtype_numeric \u001b[39mand\u001b[39;00m \u001b[39mhasattr\u001b[39m(array\u001b[39m.\u001b[39mdtype, \u001b[39m\"\u001b[39m\u001b[39mkind\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mand\u001b[39;00m array\u001b[39m.\u001b[39mdtype\u001b[39m.\u001b[39mkind \u001b[39min\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mUSV\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m 946\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 947\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mdtype=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mnumeric\u001b[39m\u001b[39m'\u001b[39m\u001b[39m is not compatible with arrays of bytes/strings.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 948\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mConvert your data to numeric values explicitly instead.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 949\u001b[0m )\n", "\u001b[0;31mValueError\u001b[0m: Expected 2D array, got 1D array instead:\narray=[0. 2. 3. 4.].\nReshape your data either using array.reshape(-1, 1) if your data has a single feature or array.reshape(1, -1) if it contains a single sample." ] } @@ -776,7 +776,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -817,84 +817,84 @@ " \n", " \n", " \n", - " 257\n", - " 50\n", - " 1\n", + " 298\n", + " 57\n", " 0\n", - " 144\n", - " 200\n", " 0\n", + " 140\n", + " 241\n", " 0\n", - " 126\n", " 1\n", - " 0.9\n", + " 123\n", + " 1\n", + " 0.2\n", " 1\n", " 0\n", " 3\n", " \n", " \n", - " 39\n", - " 65\n", - " 0\n", - " 2\n", - " 160\n", - " 360\n", + " 102\n", + " 63\n", " 0\n", + " 1\n", + " 140\n", + " 195\n", " 0\n", - " 151\n", + " 1\n", + " 179\n", " 0\n", - " 0.8\n", + " 0.0\n", + " 2\n", " 2\n", - " 0\n", " 2\n", " \n", " \n", - " 154\n", - " 39\n", + " 254\n", + " 59\n", + " 1\n", + " 3\n", + " 160\n", + " 273\n", " 0\n", - " 2\n", - " 138\n", - " 220\n", " 0\n", - " 1\n", - " 152\n", + " 125\n", " 0\n", " 0.0\n", - " 1\n", + " 2\n", " 0\n", " 2\n", " \n", " \n", - " 36\n", - " 54\n", - " 0\n", - " 2\n", - " 135\n", - " 304\n", + " 171\n", + " 48\n", + " 1\n", " 1\n", + " 110\n", + " 229\n", + " 0\n", " 1\n", - " 170\n", + " 168\n", " 0\n", - " 0.0\n", - " 2\n", + " 1.0\n", " 0\n", - " 2\n", + " 0\n", + " 3\n", " \n", " \n", - " 71\n", - " 51\n", + " 163\n", + " 38\n", " 1\n", " 2\n", - " 94\n", - " 227\n", + " 138\n", + " 175\n", " 0\n", " 1\n", - " 154\n", - " 1\n", + " 173\n", + " 0\n", " 0.0\n", " 2\n", - " 1\n", - " 3\n", + " 4\n", + " 2\n", " \n", " \n", "\n", @@ -902,18 +902,18 @@ ], "text/plain": [ " age sex cp trestbps chol fbs restecg thalach exang oldpeak \\\n", - "257 50 1 0 144 200 0 0 126 1 0.9 \n", - "39 65 0 2 160 360 0 0 151 0 0.8 \n", - "154 39 0 2 138 220 0 1 152 0 0.0 \n", - "36 54 0 2 135 304 1 1 170 0 0.0 \n", - "71 51 1 2 94 227 0 1 154 1 0.0 \n", + "298 57 0 0 140 241 0 1 123 1 0.2 \n", + "102 63 0 1 140 195 0 1 179 0 0.0 \n", + "254 59 1 3 160 273 0 0 125 0 0.0 \n", + "171 48 1 1 110 229 0 1 168 0 1.0 \n", + "163 38 1 2 138 175 0 1 173 0 0.0 \n", "\n", " slope ca thal \n", - "257 1 0 3 \n", - "39 2 0 2 \n", - "154 1 0 2 \n", - "36 2 0 2 \n", - "71 2 1 3 " + "298 1 0 3 \n", + "102 2 2 2 \n", + "254 2 0 2 \n", + "171 0 0 3 \n", + "163 2 4 2 " ] }, "execution_count": 11, @@ -928,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -957,7 +957,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -987,14 +987,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "The model's accuracy on the testing dataset is: 77.63%\n" + "The model's accuracy on the testing dataset is: 75.00%\n" ] } ], @@ -1024,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -1033,12 +1033,12 @@ "text": [ " precision recall f1-score support\n", "\n", - " 0 0.76 0.69 0.72 32\n", - " 1 0.79 0.84 0.81 44\n", + " 0 0.81 0.60 0.69 35\n", + " 1 0.72 0.88 0.79 41\n", "\n", - " accuracy 0.78 76\n", - " macro avg 0.77 0.76 0.77 76\n", - "weighted avg 0.78 0.78 0.77 76\n", + " accuracy 0.75 76\n", + " macro avg 0.76 0.74 0.74 76\n", + "weighted avg 0.76 0.75 0.74 76\n", "\n" ] } @@ -1052,14 +1052,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([[22, 10],\n", - " [ 7, 37]])" + "array([[21, 14],\n", + " [ 5, 36]])" ] }, "execution_count": 16, @@ -1075,13 +1075,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.7763157894736842" + "0.75" ] }, "execution_count": 17, @@ -1142,7 +1142,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1150,34 +1150,34 @@ "output_type": "stream", "text": [ "Trying model with 100 estimators...\n", - "Model accuracy on test set: 80.26%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 110 estimators...\n", - "Model accuracy on test set: 78.95%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 120 estimators...\n", - "Model accuracy on test set: 80.26%\n", + "Model accuracy on test set: 75.00%\n", "\n", "Trying model with 130 estimators...\n", - "Model accuracy on test set: 78.95%\n", + "Model accuracy on test set: 72.37%\n", "\n", "Trying model with 140 estimators...\n", - "Model accuracy on test set: 80.26%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 150 estimators...\n", - "Model accuracy on test set: 80.26%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 160 estimators...\n", - "Model accuracy on test set: 80.26%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 170 estimators...\n", - "Model accuracy on test set: 78.95%\n", + "Model accuracy on test set: 75.00%\n", "\n", "Trying model with 180 estimators...\n", - "Model accuracy on test set: 78.95%\n", + "Model accuracy on test set: 73.68%\n", "\n", "Trying model with 190 estimators...\n", - "Model accuracy on test set: 77.63%\n", + "Model accuracy on test set: 75.00%\n", "\n" ] } @@ -1207,7 +1207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1215,43 +1215,43 @@ "output_type": "stream", "text": [ "Trying model with 100 estimators...\n", - "Model accuracy on single test set split: 80.26%\n", + "Model accuracy on single test set split: 73.68%\n", "5-fold cross-validation score: 82.15%\n", "\n", "Trying model with 110 estimators...\n", - "Model accuracy on single test set split: 81.58%\n", + "Model accuracy on single test set split: 73.68%\n", "5-fold cross-validation score: 81.17%\n", "\n", "Trying model with 120 estimators...\n", - "Model accuracy on single test set split: 77.63%\n", + "Model accuracy on single test set split: 75.00%\n", "5-fold cross-validation score: 83.49%\n", "\n", "Trying model with 130 estimators...\n", - "Model accuracy on single test set split: 80.26%\n", + "Model accuracy on single test set split: 72.37%\n", "5-fold cross-validation score: 83.14%\n", "\n", "Trying model with 140 estimators...\n", - "Model accuracy on single test set split: 77.63%\n", + "Model accuracy on single test set split: 73.68%\n", "5-fold cross-validation score: 82.48%\n", "\n", "Trying model with 150 estimators...\n", - "Model accuracy on single test set split: 78.95%\n", + "Model accuracy on single test set split: 73.68%\n", "5-fold cross-validation score: 80.17%\n", "\n", "Trying model with 160 estimators...\n", - "Model accuracy on single test set split: 78.95%\n", + "Model accuracy on single test set split: 71.05%\n", "5-fold cross-validation score: 80.83%\n", "\n", "Trying model with 170 estimators...\n", - "Model accuracy on single test set split: 78.95%\n", + "Model accuracy on single test set split: 73.68%\n", "5-fold cross-validation score: 82.16%\n", "\n", "Trying model with 180 estimators...\n", - "Model accuracy on single test set split: 78.95%\n", + "Model accuracy on single test set split: 72.37%\n", "5-fold cross-validation score: 81.50%\n", "\n", "Trying model with 190 estimators...\n", - "Model accuracy on single test set split: 78.95%\n", + "Model accuracy on single test set split: 72.37%\n", "5-fold cross-validation score: 81.83%\n", "\n" ] @@ -1294,7 +1294,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [ { @@ -1339,7 +1339,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1371,14 +1371,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Best model score on single split of the data: 77.63%\n" + "Best model score on single split of the data: 75.00%\n" ] } ], @@ -1408,7 +1408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1420,14 +1420,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loaded pickle model prediction score: 78.95%\n" + "Loaded pickle model prediction score: 72.37%\n" ] } ], @@ -1446,7 +1446,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1469,14 +1469,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Loaded joblib model prediction score: 78.95%\n" + "Loaded joblib model prediction score: 72.37%\n" ] } ], @@ -1516,7 +1516,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -1674,7 +1674,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1947,7 +1947,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [ { @@ -1988,7 +1988,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -2015,7 +2015,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -2036,7 +2036,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -2076,7 +2076,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -2238,7 +2238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -2272,7 +2272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -2292,7 +2292,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -2302,12 +2302,12 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/var/folders/c4/qj4gdk190td18bqvjjh0p3p00000gn/T/ipykernel_27997/1044518071.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m# Try to predict with random forest on price column (doesn't work)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensemble\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 6\u001b[0m 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918\u001b[0;31m \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 919\u001b[0m raise ValueError(\n\u001b[1;32m 920\u001b[0m \u001b[0;34m\"Complex data not supported\\n{}\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 921\u001b[0m ) from complex_warning\n", + "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m 618\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m\"estimator\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m 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We convert it to an array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 383\u001b[0m \u001b[0;31m# container that is consistent with the input's namespace.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m 2082\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2083\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2084\u001b[0;31m \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2085\u001b[0m if (\n\u001b[1;32m 2086\u001b[0m \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2087\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0musing_copy_on_write\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Honda'" @@ -2370,7 +2370,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -2429,7 +2429,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -2528,7 +2528,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [ { @@ -2558,7 +2558,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -2598,7 +2598,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -2702,7 +2702,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -2948,7 +2948,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "metadata": {}, "outputs": [ { @@ -3238,7 +3238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -3294,7 +3294,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -3460,7 +3460,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, "outputs": [ { @@ -3495,7 +3495,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -3519,7 +3519,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -3545,7 +3545,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -3600,7 +3600,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "metadata": {}, "outputs": [ { @@ -3610,14 +3610,14 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[50], line 8\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[38;5;66;03m# Fit and score a model\u001b[39;00m\n\u001b[1;32m 7\u001b[0m model \u001b[38;5;241m=\u001b[39m RandomForestRegressor()\n\u001b[0;32m----> 8\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 9\u001b[0m model\u001b[38;5;241m.\u001b[39mscore(X_test, y_test)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:1151\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1144\u001b[0m estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[1;32m 1146\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[1;32m 1147\u001b[0m skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[1;32m 1148\u001b[0m prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1149\u001b[0m )\n\u001b[1;32m 1150\u001b[0m ):\n\u001b[0;32m-> 1151\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:348\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m issparse(y):\n\u001b[1;32m 347\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 348\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_data\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 349\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmulti_output\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mcsc\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mDTYPE\u001b[49m\n\u001b[1;32m 350\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 351\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m sample_weight \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 352\u001b[0m sample_weight \u001b[38;5;241m=\u001b[39m _check_sample_weight(sample_weight, X)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:621\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m 619\u001b[0m y \u001b[38;5;241m=\u001b[39m check_array(y, input_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124my\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mcheck_y_params)\n\u001b[1;32m 620\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 621\u001b[0m X, y \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_X_y\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mcheck_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 622\u001b[0m out \u001b[38;5;241m=\u001b[39m X, y\n\u001b[1;32m 624\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m no_val_X \u001b[38;5;129;01mand\u001b[39;00m check_params\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mensure_2d\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mTrue\u001b[39;00m):\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:1147\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m 1142\u001b[0m estimator_name \u001b[38;5;241m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m 1143\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1144\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m requires y to be passed, but the target y is None\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1145\u001b[0m )\n\u001b[0;32m-> 1147\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[43mcheck_array\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1148\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1149\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1150\u001b[0m \u001b[43m \u001b[49m\u001b[43maccept_large_sparse\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43maccept_large_sparse\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1151\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1152\u001b[0m \u001b[43m \u001b[49m\u001b[43morder\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morder\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1153\u001b[0m \u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcopy\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1154\u001b[0m \u001b[43m \u001b[49m\u001b[43mforce_all_finite\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1155\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_2d\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_2d\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1156\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nd\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1157\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_samples\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_samples\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1158\u001b[0m \u001b[43m \u001b[49m\u001b[43mensure_min_features\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mensure_min_features\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1159\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1160\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mX\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1161\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1163\u001b[0m y \u001b[38;5;241m=\u001b[39m _check_y(y, multi_output\u001b[38;5;241m=\u001b[39mmulti_output, y_numeric\u001b[38;5;241m=\u001b[39my_numeric, estimator\u001b[38;5;241m=\u001b[39mestimator)\n\u001b[1;32m 1165\u001b[0m check_consistent_length(X, y)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:959\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 953\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 954\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFound array with dim \u001b[39m\u001b[38;5;132;01m%d\u001b[39;00m\u001b[38;5;124m. \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m expected <= 2.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 955\u001b[0m \u001b[38;5;241m%\u001b[39m (array\u001b[38;5;241m.\u001b[39mndim, estimator_name)\n\u001b[1;32m 956\u001b[0m )\n\u001b[1;32m 958\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 959\u001b[0m \u001b[43m_assert_all_finite\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 960\u001b[0m \u001b[43m \u001b[49m\u001b[43marray\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 961\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 962\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 963\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mforce_all_finite\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mallow-nan\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 964\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 966\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ensure_min_samples \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 967\u001b[0m n_samples \u001b[38;5;241m=\u001b[39m _num_samples(array)\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:124\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m 122\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n\u001b[0;32m--> 124\u001b[0m \u001b[43m_assert_all_finite_element_wise\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 125\u001b[0m \u001b[43m \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 126\u001b[0m \u001b[43m \u001b[49m\u001b[43mxp\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mxp\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 127\u001b[0m \u001b[43m \u001b[49m\u001b[43mallow_nan\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mallow_nan\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 128\u001b[0m \u001b[43m \u001b[49m\u001b[43mmsg_dtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmsg_dtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 129\u001b[0m \u001b[43m \u001b[49m\u001b[43mestimator_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mestimator_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 130\u001b[0m \u001b[43m \u001b[49m\u001b[43minput_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 131\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:173\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimator_name \u001b[38;5;129;01mand\u001b[39;00m input_name \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m has_nan_error:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;66;03m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m 158\u001b[0m \u001b[38;5;66;03m# scikit-learn.\u001b[39;00m\n\u001b[1;32m 159\u001b[0m msg_err \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 160\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mestimator_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m does not accept missing values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 161\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 171\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m#estimators-that-handle-nan-values\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 172\u001b[0m )\n\u001b[0;32m--> 173\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg_err)\n", + "\u001b[1;32m/Users/daniel/code/zero-to-mastery-ml/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb Cell 96\u001b[0m line \u001b[0;36m8\n\u001b[1;32m 6\u001b[0m \u001b[39m# Fit and score a model\u001b[39;00m\n\u001b[1;32m 7\u001b[0m model \u001b[39m=\u001b[39m RandomForestRegressor()\n\u001b[0;32m----> 8\u001b[0m model\u001b[39m.\u001b[39;49mfit(X_train, y_train)\n\u001b[1;32m 9\u001b[0m model\u001b[39m.\u001b[39mscore(X_test, y_test)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:1152\u001b[0m, in \u001b[0;36m_fit_context..decorator..wrapper\u001b[0;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1145\u001b[0m estimator\u001b[39m.\u001b[39m_validate_params()\n\u001b[1;32m 1147\u001b[0m \u001b[39mwith\u001b[39;00m config_context(\n\u001b[1;32m 1148\u001b[0m skip_parameter_validation\u001b[39m=\u001b[39m(\n\u001b[1;32m 1149\u001b[0m prefer_skip_nested_validation \u001b[39mor\u001b[39;00m global_skip_validation\n\u001b[1;32m 1150\u001b[0m )\n\u001b[1;32m 1151\u001b[0m ):\n\u001b[0;32m-> 1152\u001b[0m \u001b[39mreturn\u001b[39;00m fit_method(estimator, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/ensemble/_forest.py:348\u001b[0m, in \u001b[0;36mBaseForest.fit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m 346\u001b[0m \u001b[39mif\u001b[39;00m issparse(y):\n\u001b[1;32m 347\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39msparse multilabel-indicator for y is not supported.\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m--> 348\u001b[0m X, y \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_validate_data(\n\u001b[1;32m 349\u001b[0m X, y, multi_output\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, accept_sparse\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mcsc\u001b[39;49m\u001b[39m\"\u001b[39;49m, dtype\u001b[39m=\u001b[39;49mDTYPE\n\u001b[1;32m 350\u001b[0m )\n\u001b[1;32m 351\u001b[0m \u001b[39mif\u001b[39;00m sample_weight \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 352\u001b[0m sample_weight \u001b[39m=\u001b[39m _check_sample_weight(sample_weight, X)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/base.py:622\u001b[0m, in \u001b[0;36mBaseEstimator._validate_data\u001b[0;34m(self, X, y, reset, validate_separately, cast_to_ndarray, **check_params)\u001b[0m\n\u001b[1;32m 620\u001b[0m y \u001b[39m=\u001b[39m check_array(y, input_name\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39my\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mcheck_y_params)\n\u001b[1;32m 621\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 622\u001b[0m X, y \u001b[39m=\u001b[39m check_X_y(X, y, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mcheck_params)\n\u001b[1;32m 623\u001b[0m out \u001b[39m=\u001b[39m X, y\n\u001b[1;32m 625\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m no_val_X \u001b[39mand\u001b[39;00m check_params\u001b[39m.\u001b[39mget(\u001b[39m\"\u001b[39m\u001b[39mensure_2d\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mTrue\u001b[39;00m):\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:1146\u001b[0m, in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m 1141\u001b[0m estimator_name \u001b[39m=\u001b[39m _check_estimator_name(estimator)\n\u001b[1;32m 1142\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1143\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m requires y to be passed, but the target y is None\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 1144\u001b[0m )\n\u001b[0;32m-> 1146\u001b[0m X \u001b[39m=\u001b[39m check_array(\n\u001b[1;32m 1147\u001b[0m X,\n\u001b[1;32m 1148\u001b[0m accept_sparse\u001b[39m=\u001b[39;49maccept_sparse,\n\u001b[1;32m 1149\u001b[0m accept_large_sparse\u001b[39m=\u001b[39;49maccept_large_sparse,\n\u001b[1;32m 1150\u001b[0m dtype\u001b[39m=\u001b[39;49mdtype,\n\u001b[1;32m 1151\u001b[0m order\u001b[39m=\u001b[39;49morder,\n\u001b[1;32m 1152\u001b[0m copy\u001b[39m=\u001b[39;49mcopy,\n\u001b[1;32m 1153\u001b[0m force_all_finite\u001b[39m=\u001b[39;49mforce_all_finite,\n\u001b[1;32m 1154\u001b[0m ensure_2d\u001b[39m=\u001b[39;49mensure_2d,\n\u001b[1;32m 1155\u001b[0m allow_nd\u001b[39m=\u001b[39;49mallow_nd,\n\u001b[1;32m 1156\u001b[0m ensure_min_samples\u001b[39m=\u001b[39;49mensure_min_samples,\n\u001b[1;32m 1157\u001b[0m ensure_min_features\u001b[39m=\u001b[39;49mensure_min_features,\n\u001b[1;32m 1158\u001b[0m estimator\u001b[39m=\u001b[39;49mestimator,\n\u001b[1;32m 1159\u001b[0m input_name\u001b[39m=\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39mX\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 1160\u001b[0m )\n\u001b[1;32m 1162\u001b[0m y \u001b[39m=\u001b[39m _check_y(y, multi_output\u001b[39m=\u001b[39mmulti_output, y_numeric\u001b[39m=\u001b[39my_numeric, estimator\u001b[39m=\u001b[39mestimator)\n\u001b[1;32m 1164\u001b[0m check_consistent_length(X, y)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:957\u001b[0m, in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m 951\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 952\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mFound array with dim \u001b[39m\u001b[39m%d\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m expected <= 2.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 953\u001b[0m \u001b[39m%\u001b[39m (array\u001b[39m.\u001b[39mndim, estimator_name)\n\u001b[1;32m 954\u001b[0m )\n\u001b[1;32m 956\u001b[0m \u001b[39mif\u001b[39;00m force_all_finite:\n\u001b[0;32m--> 957\u001b[0m _assert_all_finite(\n\u001b[1;32m 958\u001b[0m array,\n\u001b[1;32m 959\u001b[0m input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m 960\u001b[0m estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m 961\u001b[0m allow_nan\u001b[39m=\u001b[39;49mforce_all_finite \u001b[39m==\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39mallow-nan\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m 962\u001b[0m )\n\u001b[1;32m 964\u001b[0m \u001b[39mif\u001b[39;00m ensure_min_samples \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 965\u001b[0m n_samples \u001b[39m=\u001b[39m _num_samples(array)\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:122\u001b[0m, in \u001b[0;36m_assert_all_finite\u001b[0;34m(X, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 119\u001b[0m \u001b[39mif\u001b[39;00m first_pass_isfinite:\n\u001b[1;32m 120\u001b[0m \u001b[39mreturn\u001b[39;00m\n\u001b[0;32m--> 122\u001b[0m _assert_all_finite_element_wise(\n\u001b[1;32m 123\u001b[0m X,\n\u001b[1;32m 124\u001b[0m xp\u001b[39m=\u001b[39;49mxp,\n\u001b[1;32m 125\u001b[0m allow_nan\u001b[39m=\u001b[39;49mallow_nan,\n\u001b[1;32m 126\u001b[0m msg_dtype\u001b[39m=\u001b[39;49mmsg_dtype,\n\u001b[1;32m 127\u001b[0m estimator_name\u001b[39m=\u001b[39;49mestimator_name,\n\u001b[1;32m 128\u001b[0m input_name\u001b[39m=\u001b[39;49minput_name,\n\u001b[1;32m 129\u001b[0m )\n", + "File \u001b[0;32m~/code/zero-to-mastery-ml/env/lib/python3.10/site-packages/sklearn/utils/validation.py:171\u001b[0m, in \u001b[0;36m_assert_all_finite_element_wise\u001b[0;34m(X, xp, allow_nan, msg_dtype, estimator_name, input_name)\u001b[0m\n\u001b[1;32m 154\u001b[0m \u001b[39mif\u001b[39;00m estimator_name \u001b[39mand\u001b[39;00m input_name \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mX\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mand\u001b[39;00m has_nan_error:\n\u001b[1;32m 155\u001b[0m \u001b[39m# Improve the error message on how to handle missing values in\u001b[39;00m\n\u001b[1;32m 156\u001b[0m \u001b[39m# scikit-learn.\u001b[39;00m\n\u001b[1;32m 157\u001b[0m msg_err \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m (\n\u001b[1;32m 158\u001b[0m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m{\u001b[39;00mestimator_name\u001b[39m}\u001b[39;00m\u001b[39m does not accept missing values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 159\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m encoded as NaN natively. For supervised learning, you might want\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 169\u001b[0m \u001b[39m\"\u001b[39m\u001b[39m#estimators-that-handle-nan-values\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 170\u001b[0m )\n\u001b[0;32m--> 171\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(msg_err)\n", "\u001b[0;31mValueError\u001b[0m: Input X contains NaN.\nRandomForestRegressor does not accept missing values encoded as NaN natively. For supervised learning, you might want to consider sklearn.ensemble.HistGradientBoostingClassifier and Regressor which accept missing values encoded as NaNs natively. Alternatively, it is possible to preprocess the data, for instance by using an imputer transformer in a pipeline or drop samples with missing values. See https://scikit-learn.org/stable/modules/impute.html You can find a list of all estimators that handle NaN values at the following page: https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values" ] } @@ -3642,32 +3642,35 @@ "\n", "When we try to fit it on a dataset with missing samples, Scikit-Learn produces the error:\n", "\n", - "```\n", - "ValueError: Input X contains NaN.\n", - "RandomForestRegressor does not accept missing values encoded as NaN natively...\n", - "```\n", + "`ValueError: Input X contains NaN. RandomForestRegressor does not accept missing values encoded as NaN natively...`\n", "\n", "Looks like if we want to use `RandomForestRegressor`, we'll have to either fill or remove the missing values.\n", "\n", - "> **Note:** Scikit-Learn does have a [list of models which can handle NaNs or missing values directly](https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values). Such as, [`sklearn.ensemble.HistGradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html) or [`sklearn.ensemble.HistGradientBoostingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html).\n", - ">\n", - "> As an experiment, you may want to try the following:\n", - ">\n", - "> ```python\n", - "> from sklearn.ensemble import HistGradientBoostingRegressor\n", - ">\n", - "># Try a model that can handle NaNs natively\n", - ">nan_model = HistGradientBoostingRegressor()\n", - ">nan_model.fit(X_train, y_train)\n", - ">nan_model.score(X_test, y_test)\n", - ">```\n", - "\n", + "
\n", + " Note: Scikit-Learn does have a \n", + " list of models which can handle NaNs or missing values directly.\n", + " \n", + "

Such as, \n", + " sklearn.ensemble.HistGradientBoostingClassifier \n", + " or \n", + " sklearn.ensemble.HistGradientBoostingRegressor.\n", + "

\n", + "

As an experiment, you may want to try the following:

\n", + " 
\n",
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "\n",
+    "\\# Try a model that can handle NaNs natively\n",
+    "nan_model = HistGradientBoostingRegressor()\n",
+    "nan_model.fit(X_train, y_train)\n",
+    "nan_model.score(X_test, y_test)\n",
+    "
\n", + "
\n", "Let's see what values are missing again." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "metadata": {}, "outputs": [ { @@ -3734,7 +3737,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ @@ -3751,7 +3754,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "metadata": {}, "outputs": [], "source": [ @@ -3768,7 +3771,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "metadata": {}, "outputs": [ { @@ -3802,7 +3805,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "metadata": {}, "outputs": [ { @@ -3827,7 +3830,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -3844,7 +3847,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -3861,7 +3864,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "metadata": {}, "outputs": [ { @@ -3898,7 +3901,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -3915,7 +3918,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [ { @@ -3948,7 +3951,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 61, "metadata": {}, "outputs": [ { @@ -3986,7 +3989,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "metadata": {}, "outputs": [ { @@ -4015,7 +4018,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "metadata": {}, "outputs": [ { @@ -4061,7 +4064,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "metadata": {}, "outputs": [ { @@ -4118,7 +4121,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 65, "metadata": {}, "outputs": [ { @@ -4150,7 +4153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 66, "metadata": {}, "outputs": [ { @@ -4184,7 +4187,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 67, "metadata": {}, "outputs": [], "source": [ @@ -4201,7 +4204,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 68, "metadata": {}, "outputs": [ { @@ -4235,7 +4238,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 69, "metadata": {}, "outputs": [], "source": [ @@ -4269,7 +4272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 70, "metadata": {}, "outputs": [], "source": [ @@ -4300,7 +4303,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, "outputs": [], "source": [ @@ -4344,7 +4347,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 72, "metadata": {}, "outputs": [], "source": [ @@ -4383,7 +4386,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 73, "metadata": {}, "outputs": [ { @@ -4425,7 +4428,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -4464,7 +4467,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 75, "metadata": {}, "outputs": [ { @@ -4496,7 +4499,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 76, "metadata": {}, "outputs": [ { @@ -4535,7 +4538,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -4634,7 +4637,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 78, "metadata": {}, "outputs": [ { @@ -4692,7 +4695,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 79, "metadata": {}, "outputs": [ { @@ -4786,7 +4789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -4805,7 +4808,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 81, "metadata": {}, "outputs": [ { @@ -4934,7 +4937,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -4980,7 +4983,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 83, "metadata": {}, "outputs": [ { @@ -5042,7 +5045,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -5109,7 +5112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 85, "metadata": {}, "outputs": [ { @@ -5267,7 +5270,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 86, "metadata": {}, "outputs": [ { @@ -5307,7 +5310,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 87, "metadata": {}, "outputs": [ { @@ -5369,7 +5372,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 88, "metadata": {}, "outputs": [ { @@ -5497,7 +5500,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 89, "metadata": {}, "outputs": [ { @@ -5548,7 +5551,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 90, "metadata": {}, "outputs": [ { @@ -5706,7 +5709,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 91, "metadata": {}, "outputs": [ { @@ -5770,7 +5773,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 92, "metadata": {}, "outputs": [ { @@ -5804,7 +5807,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 93, "metadata": {}, "outputs": [ { @@ -5835,7 +5838,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 94, "metadata": {}, "outputs": [ { @@ -5863,7 +5866,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 95, "metadata": {}, "outputs": [ { @@ -5895,7 +5898,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 96, "metadata": {}, "outputs": [ { @@ -5925,7 +5928,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -5955,7 +5958,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 98, "metadata": {}, "outputs": [ { @@ -5989,7 +5992,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -6023,7 +6026,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 100, "metadata": {}, "outputs": [ { @@ -6086,7 +6089,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 101, "metadata": {}, "outputs": [], "source": [ @@ -6119,7 +6122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 102, "metadata": {}, "outputs": [ { @@ -6164,7 +6167,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 103, "metadata": {}, "outputs": [], "source": [ @@ -6195,7 +6198,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 104, "metadata": {}, "outputs": [ { @@ -6248,7 +6251,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 105, "metadata": {}, "outputs": [], "source": [ @@ -6286,7 +6289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 106, "metadata": {}, "outputs": [ { @@ -6307,7 +6310,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "metadata": {}, "outputs": [ { @@ -6374,7 +6377,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "metadata": {}, "outputs": [ { @@ -6404,7 +6407,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "metadata": {}, "outputs": [ { @@ -6447,7 +6450,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "metadata": {}, "outputs": [ { @@ -6500,7 +6503,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "metadata": {}, "outputs": [ { @@ -6545,7 +6548,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "metadata": {}, "outputs": [ { @@ -6585,7 +6588,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "metadata": {}, "outputs": [ { @@ -6632,12 +6635,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -6685,7 +6688,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -6694,7 +6697,7 @@ "0.9304956896551724" ] }, - "execution_count": 130, + "execution_count": 115, "metadata": {}, "output_type": "execute_result" } @@ -6719,12 +6722,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -6753,12 +6756,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -6775,7 +6778,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "metadata": {}, "outputs": [ { @@ -6784,7 +6787,7 @@ "1.0" ] }, - "execution_count": 131, + "execution_count": 118, "metadata": {}, "output_type": "execute_result" } @@ -6816,7 +6819,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "metadata": {}, "outputs": [ { @@ -6826,7 +6829,7 @@ " [ 4, 28]])" ] }, - "execution_count": 132, + "execution_count": 119, "metadata": {}, "output_type": "execute_result" } @@ -6850,7 +6853,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "metadata": {}, "outputs": [ { @@ -6905,7 +6908,7 @@ "1 4 28" ] }, - "execution_count": 133, + "execution_count": 120, "metadata": {}, "output_type": "execute_result" } @@ -6938,12 +6941,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -6960,12 +6963,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -6997,7 +7000,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "metadata": {}, "outputs": [ { @@ -7053,7 +7056,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 124, "metadata": {}, "outputs": [ { @@ -7129,7 +7132,7 @@ "support 9999.00000 1.0 0.9999 10000.000000 10000.00000" ] }, - "execution_count": 137, + "execution_count": 124, "metadata": {}, "output_type": "execute_result" } @@ -7183,7 +7186,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 125, "metadata": {}, "outputs": [], "source": [ @@ -7216,7 +7219,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 126, "metadata": {}, "outputs": [ { @@ -7225,7 +7228,7 @@ "0.8059809073051385" ] }, - "execution_count": 139, + "execution_count": 126, "metadata": {}, "output_type": "execute_result" } @@ -7246,7 +7249,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 127, "metadata": {}, "outputs": [ { @@ -7255,7 +7258,7 @@ "0.0" ] }, - "execution_count": 140, + "execution_count": 127, "metadata": {}, "output_type": "execute_result" } @@ -7278,7 +7281,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 128, "metadata": {}, "outputs": [ { @@ -7287,7 +7290,7 @@ "1.0" ] }, - "execution_count": 141, + "execution_count": 128, "metadata": {}, "output_type": "execute_result" } @@ -7314,7 +7317,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 129, "metadata": {}, "outputs": [ { @@ -7323,7 +7326,7 @@ "0.3270458119670544" ] }, - "execution_count": 142, + "execution_count": 129, "metadata": {}, "output_type": "execute_result" } @@ -7350,7 +7353,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 130, "metadata": {}, "outputs": [ { @@ -7456,7 +7459,7 @@ "[4128 rows x 2 columns]" ] }, - "execution_count": 143, + "execution_count": 130, "metadata": {}, "output_type": "execute_result" } @@ -7479,12 +7482,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 131, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -7514,7 +7517,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 132, "metadata": {}, "outputs": [ { @@ -7523,7 +7526,7 @@ "0.2542443610174998" ] }, - "execution_count": 145, + "execution_count": 132, "metadata": {}, "output_type": "execute_result" } @@ -7570,7 +7573,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 133, "metadata": {}, "outputs": [], "source": [ @@ -7594,7 +7597,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 134, "metadata": {}, "outputs": [ { @@ -7603,7 +7606,7 @@ "array([0.81967213, 0.90163934, 0.83606557, 0.78333333, 0.78333333])" ] }, - "execution_count": 147, + "execution_count": 134, "metadata": {}, "output_type": "execute_result" } @@ -7625,7 +7628,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 135, "metadata": {}, "outputs": [ { @@ -7650,7 +7653,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 136, "metadata": {}, "outputs": [ { @@ -7678,7 +7681,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 137, "metadata": {}, "outputs": [ { @@ -7704,7 +7707,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 138, "metadata": {}, "outputs": [ { @@ -7730,7 +7733,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 139, "metadata": {}, "outputs": [ { @@ -7758,7 +7761,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 140, "metadata": {}, "outputs": [], "source": [ @@ -7782,7 +7785,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 141, "metadata": {}, "outputs": [ { @@ -7808,7 +7811,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -7841,7 +7844,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 143, "metadata": {}, "outputs": [ { @@ -7893,7 +7896,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 144, "metadata": {}, "outputs": [ { @@ -7950,7 +7953,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 145, "metadata": {}, "outputs": [ { @@ -8056,7 +8059,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 146, "metadata": {}, "outputs": [], "source": [ @@ -8076,7 +8079,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 147, "metadata": {}, "outputs": [ { @@ -8102,7 +8105,7 @@ " 'warm_start': False}" ] }, - "execution_count": 160, + "execution_count": 147, "metadata": {}, "output_type": "execute_result" } @@ -8187,7 +8190,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 148, "metadata": {}, "outputs": [ { @@ -8213,7 +8216,7 @@ " 'warm_start': False}" ] }, - "execution_count": 161, + "execution_count": 148, "metadata": {}, "output_type": "execute_result" } @@ -8244,7 +8247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 149, "metadata": {}, "outputs": [], "source": [ @@ -8282,7 +8285,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 150, "metadata": {}, "outputs": [ { @@ -8301,7 +8304,7 @@ "{'accuracy': 0.8, 'precision': 0.78, 'recall': 0.88, 'f1': 0.82}" ] }, - "execution_count": 163, + "execution_count": 150, "metadata": {}, "output_type": "execute_result" } @@ -8340,7 +8343,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 151, "metadata": {}, "outputs": [ { @@ -8371,7 +8374,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 152, "metadata": {}, "outputs": [ { @@ -8441,7 +8444,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 153, "metadata": {}, "outputs": [], "source": [ @@ -8476,7 +8479,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 154, "metadata": {}, "outputs": [ { @@ -8518,7 +8521,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 155, "metadata": {}, "outputs": [ { @@ -8529,13 +8532,19 @@ "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", + "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", - "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", - "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.4s\n", + "[CV] END max_depth=10, max_features=None, min_samples_leaf=8, min_samples_split=2, n_estimators=500; total time= 0.5s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=10; total time= 0.0s\n", @@ -8577,8 +8586,8 @@ "[CV] END max_depth=5, max_features=log2, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=log2, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=None, max_features=None, min_samples_leaf=4, min_samples_split=4, n_estimators=1200; total time= 1.0s\n", @@ -8590,20 +8599,20 @@ "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=5, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=5, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", + "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.1s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.0s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.0s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.0s\n", - "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.0s\n", - "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.0s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=20, max_features=None, min_samples_leaf=2, min_samples_split=8, n_estimators=1200; total time= 1.1s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=8, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=10; total time= 0.0s\n", @@ -8626,10 +8635,10 @@ "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=20, max_features=None, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time= 0.2s\n", + "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.5s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.4s\n", - "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.4s\n", - "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.4s\n", + "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.5s\n", "[CV] END max_depth=5, max_features=None, min_samples_leaf=1, min_samples_split=6, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=1, min_samples_split=8, n_estimators=200; total time= 0.2s\n", @@ -8664,7 +8673,7 @@ "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.4s\n", - "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.5s\n", + "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=500; total time= 0.4s\n", "[CV] END max_depth=None, max_features=None, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=None, max_features=None, min_samples_leaf=8, min_samples_split=4, n_estimators=10; total time= 0.0s\n", @@ -8676,7 +8685,7 @@ "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time= 0.0s\n", "[CV] END max_depth=None, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=10; total time= 0.0s\n", - "[INFO] Total time taken for 30 random combinations of hyperparameters: 48.88 seconds.\n" + "[INFO] Total time taken for 30 random combinations of hyperparameters: 49.50 seconds.\n" ] } ], @@ -8724,7 +8733,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 156, "metadata": {}, "outputs": [ { @@ -8737,7 +8746,7 @@ " 'max_depth': 30}" ] }, - "execution_count": 169, + "execution_count": 156, "metadata": {}, "output_type": "execute_result" } @@ -8756,7 +8765,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 157, "metadata": {}, "outputs": [ { @@ -8806,7 +8815,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 158, "metadata": {}, "outputs": [ { @@ -8819,7 +8828,7 @@ " 'min_samples_leaf': [1, 2, 4, 8]}" ] }, - "execution_count": 171, + "execution_count": 158, "metadata": {}, "output_type": "execute_result" } @@ -8856,7 +8865,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 159, "metadata": {}, "outputs": [], "source": [ @@ -8877,7 +8886,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 160, "metadata": {}, "outputs": [ { @@ -8908,7 +8917,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 161, "metadata": {}, "outputs": [ { @@ -8918,7 +8927,13 @@ "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", - "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", + "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=30, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", @@ -8961,21 +8976,21 @@ "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", + "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", - "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.9s\n", - "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", @@ -8993,7 +9008,7 @@ "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=40, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", @@ -9001,11 +9016,11 @@ "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=200; total time= 0.2s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=2, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=200; total time= 0.2s\n", @@ -9014,18 +9029,18 @@ "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.9s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=4, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.9s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time= 0.2s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=200; total time= 0.2s\n", @@ -9034,7 +9049,7 @@ "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", - "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n", + "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.9s\n", "[CV] END max_depth=50, max_features=log2, min_samples_leaf=4, min_samples_split=8, n_estimators=1000; total time= 0.8s\n" ] } @@ -9073,14 +9088,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 162, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[INFO] The total running time for running GridSearchCV was 61.67 seconds.\n" + "[INFO] The total running time for running GridSearchCV was 61.45 seconds.\n" ] } ], @@ -9099,7 +9114,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 163, "metadata": {}, "outputs": [ { @@ -9112,7 +9127,7 @@ " 'n_estimators': 200}" ] }, - "execution_count": 176, + "execution_count": 163, "metadata": {}, "output_type": "execute_result" } @@ -9131,7 +9146,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 164, "metadata": {}, "outputs": [ { @@ -9150,7 +9165,7 @@ "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}" ] }, - "execution_count": 177, + "execution_count": 164, "metadata": {}, "output_type": "execute_result" } @@ -9173,12 +9188,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 165, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -9237,7 +9252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 166, "metadata": {}, "outputs": [], "source": [ @@ -9257,7 +9272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 167, "metadata": {}, "outputs": [], "source": [ @@ -9274,7 +9289,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 168, "metadata": {}, "outputs": [ { @@ -9293,7 +9308,7 @@ "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}" ] }, - "execution_count": 181, + "execution_count": 168, "metadata": {}, "output_type": "execute_result" } @@ -9314,7 +9329,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 169, "metadata": {}, "outputs": [ { @@ -9323,7 +9338,7 @@ "True" ] }, - "execution_count": 182, + "execution_count": 169, "metadata": {}, "output_type": "execute_result" } @@ -9345,7 +9360,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 170, "metadata": {}, "outputs": [ { @@ -9354,7 +9369,7 @@ "['gs_random_forest_model_1.joblib']" ] }, - "execution_count": 183, + "execution_count": 170, "metadata": {}, "output_type": "execute_result" } @@ -9376,7 +9391,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 171, "metadata": {}, "outputs": [], "source": [ @@ -9393,7 +9408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 172, "metadata": {}, "outputs": [ { @@ -9412,7 +9427,7 @@ "{'accuracy': 0.89, 'precision': 0.88, 'recall': 0.91, 'f1': 0.89}" ] }, - "execution_count": 185, + "execution_count": 172, "metadata": {}, "output_type": "execute_result" } @@ -9433,7 +9448,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 173, "metadata": {}, "outputs": [ { @@ -9442,7 +9457,7 @@ "True" ] }, - "execution_count": 186, + "execution_count": 173, "metadata": {}, "output_type": "execute_result" } @@ -9497,7 +9512,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 174, "metadata": {}, "outputs": [ { @@ -9582,7 +9597,7 @@ "4 Nissan Blue 181577.0 3.0 14043.0" ] }, - "execution_count": 187, + "execution_count": 174, "metadata": {}, "output_type": "execute_result" } @@ -9594,7 +9609,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 175, "metadata": {}, "outputs": [ { @@ -9608,7 +9623,7 @@ "dtype: object" ] }, - "execution_count": 188, + "execution_count": 175, "metadata": {}, "output_type": "execute_result" } @@ -9619,7 +9634,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 176, "metadata": {}, "outputs": [ { @@ -9633,7 +9648,7 @@ "dtype: int64" ] }, - "execution_count": 189, + "execution_count": 176, "metadata": {}, "output_type": "execute_result" } @@ -9664,16 +9679,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 177, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.22188417408787864" + "0.22188417408787875" ] }, - "execution_count": 190, + "execution_count": 177, "metadata": {}, "output_type": "execute_result" } @@ -9757,7 +9772,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 178, "metadata": {}, "outputs": [ { @@ -9782,7 +9797,7 @@ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", + "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", @@ -9796,15 +9811,15 @@ "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", + "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", + "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", - "[CV] END model__max_depth=None, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.9s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", @@ -9815,7 +9830,7 @@ "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", - "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", + "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", @@ -9824,7 +9839,7 @@ "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", - "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.7s\n", + "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=2, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=mean; total time= 0.1s\n", @@ -9836,11 +9851,11 @@ "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=100, preprocessor__num__imputer__strategy=median; total time= 0.1s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", + "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", + "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.7s\n", - "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.9s\n", - "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=mean; total time= 0.8s\n", - "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.8s\n", + "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.7s\n", "[CV] END model__max_depth=5, model__max_features=sqrt, model__min_samples_split=4, model__n_estimators=1000, preprocessor__num__imputer__strategy=median; total time= 0.7s\n", @@ -9971,7 +9986,7 @@ " verbose=2)" ] }, - "execution_count": 191, + "execution_count": 178, "metadata": {}, "output_type": "execute_result" } @@ -9999,16 +10014,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 179, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.2848784564026803" + "0.2848784564026805" ] }, - "execution_count": 192, + "execution_count": 179, "metadata": {}, "output_type": "execute_result" } @@ -10071,7 +10086,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.0" + "version": "3.10.12" }, "vscode": { "interpreter": { diff --git a/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb b/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb index 89a9ba162..dd8f935ab 100644 --- a/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb +++ b/section-2-data-science-and-ml-tools/introduction-to-scikit-learn.ipynb @@ -3642,26 +3642,29 @@ "\n", "When we try to fit it on a dataset with missing samples, Scikit-Learn produces the error:\n", "\n", - "```\n", - "ValueError: Input X contains NaN.\n", - "RandomForestRegressor does not accept missing values encoded as NaN natively...\n", - "```\n", + "`ValueError: Input X contains NaN. RandomForestRegressor does not accept missing values encoded as NaN natively...`\n", "\n", "Looks like if we want to use `RandomForestRegressor`, we'll have to either fill or remove the missing values.\n", "\n", - "> **Note:** Scikit-Learn does have a [list of models which can handle NaNs or missing values directly](https://scikit-learn.org/stable/modules/impute.html#estimators-that-handle-nan-values). Such as, [`sklearn.ensemble.HistGradientBoostingClassifier`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingClassifier.html) or [`sklearn.ensemble.HistGradientBoostingRegressor`](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.HistGradientBoostingRegressor.html).\n", - ">\n", - "> As an experiment, you may want to try the following:\n", - ">\n", - "> ```python\n", - "> from sklearn.ensemble import HistGradientBoostingRegressor\n", - ">\n", - "># Try a model that can handle NaNs natively\n", - ">nan_model = HistGradientBoostingRegressor()\n", - ">nan_model.fit(X_train, y_train)\n", - ">nan_model.score(X_test, y_test)\n", - ">```\n", - "\n", + "
\n", + " Note: Scikit-Learn does have a \n", + " list of models which can handle NaNs or missing values directly.\n", + " \n", + "

Such as, \n", + " sklearn.ensemble.HistGradientBoostingClassifier \n", + " or \n", + " sklearn.ensemble.HistGradientBoostingRegressor.\n", + "

\n", + "

As an experiment, you may want to try the following:

\n", + " 
\n",
+    "from sklearn.ensemble import HistGradientBoostingRegressor\n",
+    "\n",
+    "\\# Try a model that can handle NaNs natively\n",
+    "nan_model = HistGradientBoostingRegressor()\n",
+    "nan_model.fit(X_train, y_train)\n",
+    "nan_model.score(X_test, y_test)\n",
+    "
\n", + "
\n", "Let's see what values are missing again." ] },