Multiple predictive Machine Learning models are trained on Breast Cancer data for potentially ascertaining between malignant and benign tumor.
Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Also can be found on UCI Machine Learning Repository.
- ID number
- Diagnosis (M = malignant, B = benign) 3-32)
Ten real-valued features are computed for each cell nucleus:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- fractal dimension ("coastline approximation" - 1)
The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.
All feature values are recoded with four significant digits. lissing attribute values: none Class distribution: 357 benign, 212 malignant
{
"features": [
"radius_mean",
"texture_mean",
"perimeter_mean",
"area_mean",
"smoothness_mean",
"compactness_mean",
"concavity_mean",
"radius_worst",
"texture_worst",
"perimeter_worst",
"area_worst",
"smoothness_worst",
"compactness_worst",
"concavity_worst",
"concave points_worst",
"symmetry_worst",
"fractal_dimension_worst"
]
}
-
Minimum and Maximum values of each feature
Feature Min Value Max Value radius_mean 6.981000 28.110000 ractal_dimension_worst 9.710000 39.280000 ymmetry_worst 43.790000 188.500000 oncave_points_worst 143.500000 2501.000000 oncavity_worst 0.052630 0.163400 ompactness_worst 0.019380 0.345400 moothness_worst 0.000000 0.426800 rea_worst 7.930000 36.040000 erimeter_worst 12.020000 49.540000 exture_worst 50.410000 251.200000 adius_worst 185.200000 4254.000000 oncavity_mean 0.071170 0.222600 ompactness_mean 0.027290 1.058000 moothness_mean 0.000000 1.252000 rea_mean 0.000000 0.291000 erimeter_mean 0.156500 0.663800 texture_mean 0.055040 0.207500
- Logistic Regressor
- Support Vector Classifier
- Random Forest Classifier
- Decision Tree Classifier
Below are the learning curves of models which did not undergo hyperparameter optimization.
Model | Learning Curves |
---|---|
Logistic Regressor | ![]() |
Support Vector Classifier | ![]() |
Random Forest Classifier | ![]() |
Decision Tree Clasifier | ![]() |
Using sklearn.model_selection.RandomizedSearchCV
and
sklearn.model_selection.GridSearchCV
the parameter space for each model has
been searched with parallel computing(n_jobs=-1
).
{
"logistic_regression_params": {
"random_state": 0,
"solver": "liblinear",
"tol": 0.0001
},
"svc_params": {
"C": 10,
"gamma": 0.0001,
"kernel": "rbf"
},
"random_forest_classifier_params": {
"max_depth": 80,
"min_sample_leaf": 4,
"min_sample_split": 5,
"n_estimator": 600
},
"decision_tree_params": {
"criterion": "entropy",
"max_depth": 3,
"min_samples_leaf": 10
}
}
Model | Learning Curves |
---|---|
Logistic Regressor | ![]() |
Support Vector Classifier | ![]() |
Random Forest Classifier | ![]() |
Decision Tree Classifier | ![]() |
numpy==1.21.4
pandas==1.3.4
scikit_learn==1.0.1
streamlit==1.3.0