diff --git a/docs/source/examples/Hyperband-synthetic-example.ipynb b/docs/source/examples/Hyperband-synthetic-example.ipynb
new file mode 100644
index 000000000..8bbfcc4ef
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+++ b/docs/source/examples/Hyperband-synthetic-example.ipynb
@@ -0,0 +1,4113 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'/Users/ssievert/Developer/stsievert/dask-ml/dask_ml/__init__.py'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from time import time\n",
+    "\n",
+    "import numpy as np\n",
+    "import scipy.stats as stats\n",
+    "import dask.array as da\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.linear_model import SGDClassifier\n",
+    "import sklearn\n",
+    "\n",
+    "import dask_ml\n",
+    "from dask_ml.datasets import make_classification\n",
+    "from dask_ml.wrappers import Incremental\n",
+    "from dask_ml.model_selection import HyperbandCV\n",
+    "import dask_searchcv\n",
+    "\n",
+    "from time import sleep\n",
+    "dask_ml.__file__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from distributed import Client, LocalCluster\n",
+    "procs, threads = 8, 8\n",
+    "num_workers = procs * threads\n",
+    "cluster = LocalCluster(n_workers=procs, threads_per_worker=threads)\n",
+    "client = Client(cluster)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n, d = int(10e3), int(100)\n",
+    "X, y = make_classification(n_features=d, n_samples=n,\n",
+    "                           chunks=(n // 10, d))\n",
+    "classes = da.unique(y)\n",
+    "\n",
+    "kwargs = dict(loss='hinge', penalty='elasticnet',\n",
+    "              max_iter=1.0, warm_start=True)\n",
+    "model = Incremental(SGDClassifier(), **kwargs)\n",
+    "params = {'alpha': np.logspace(-4, 0, num=1000),\n",
+    "          'l1_ratio': stats.uniform(0, 1),\n",
+    "          'average': [True, False]}\n",
+    "partial_fit_kwargs = {'classes': classes}\n",
+    "\n",
+    "from dask_ml.utils import ConstantFunction\n",
+    "model = ConstantFunction()\n",
+    "params = {'value': stats.uniform(0, 1)}\n",
+    "sleep = 0.1\n",
+    "partial_fit_kwargs = {'sleep': sleep}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fastest possible time = 2.2171875s\n"
+     ]
+    }
+   ],
+   "source": [
+    "n, d = int(1000), int(200)\n",
+    "X, y = make_classification(n_features=d, n_samples=n,\n",
+    "                           chunks=(n//10, d))\n",
+    "alg = HyperbandCV(model, params, max_iter=81)\n",
+    "\n",
+    "info = alg.info()\n",
+    "\n",
+    "train_time = sleep * info['total_partial_fit_calls']\n",
+    "best_time = train_time / num_workers\n",
+    "print(f\"Fastest possible time = {best_time}s\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Actual time = 9.322114944458008\n"
+     ]
+    }
+   ],
+   "source": [
+    "start = time()\n",
+    "alg.fit(X, y, **partial_fit_kwargs)\n",
+    "actual_time = time() - start\n",
+    "print(f\"Actual time = {actual_time}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bracket=4\n",
+      "[0 1 2 3]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>bracket</th>\n",
+       "      <th>bracket_iter</th>\n",
+       "      <th>iterations</th>\n",
+       "      <th>mean_copy_time</th>\n",
+       "      <th>mean_fit_time</th>\n",
+       "      <th>mean_score_time</th>\n",
+       "      <th>mean_test_score</th>\n",
+       "      <th>mean_train_score</th>\n",
+       "      <th>model_id</th>\n",
+       "      <th>num_models</th>\n",
+       "      <th>partial_fit_calls</th>\n",
+       "      <th>score</th>\n",
+       "      <th>std_fit_time</th>\n",
+       "      <th>std_score_time</th>\n",
+       "      <th>std_test_score</th>\n",
+       "      <th>std_train_score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>201</th>\n",
+       "      <td>bracket=0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.000201</td>\n",
+       "      <td>8.434438</td>\n",
+       "      <td>0.000133</td>\n",
+       "      <td>0.363630</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bracket=0-0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.363630</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>202</th>\n",
+       "      <td>bracket=0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.000197</td>\n",
+       "      <td>8.429263</td>\n",
+       "      <td>0.000114</td>\n",
+       "      <td>0.251782</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bracket=0-3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.251782</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>203</th>\n",
+       "      <td>bracket=0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.000159</td>\n",
+       "      <td>8.425436</td>\n",
+       "      <td>0.000097</td>\n",
+       "      <td>0.971782</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bracket=0-1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.971782</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204</th>\n",
+       "      <td>bracket=1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.000423</td>\n",
+       "      <td>8.429341</td>\n",
+       "      <td>0.000240</td>\n",
+       "      <td>0.703019</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bracket=1-7</td>\n",
+       "      <td>2</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.703019</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>205</th>\n",
+       "      <td>bracket=1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.000386</td>\n",
+       "      <td>8.430312</td>\n",
+       "      <td>0.000208</td>\n",
+       "      <td>0.942910</td>\n",
+       "      <td>None</td>\n",
+       "      <td>bracket=1-4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>81</td>\n",
+       "      <td>0.942910</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>None</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       bracket  bracket_iter  iterations  mean_copy_time  mean_fit_time  \\\n",
+       "201  bracket=0             0          81        0.000201       8.434438   \n",
+       "202  bracket=0             0          81        0.000197       8.429263   \n",
+       "203  bracket=0             0          81        0.000159       8.425436   \n",
+       "204  bracket=1             1          81        0.000423       8.429341   \n",
+       "205  bracket=1             1          81        0.000386       8.430312   \n",
+       "\n",
+       "     mean_score_time  mean_test_score mean_train_score     model_id  \\\n",
+       "201         0.000133         0.363630             None  bracket=0-0   \n",
+       "202         0.000114         0.251782             None  bracket=0-3   \n",
+       "203         0.000097         0.971782             None  bracket=0-1   \n",
+       "204         0.000240         0.703019             None  bracket=1-7   \n",
+       "205         0.000208         0.942910             None  bracket=1-4   \n",
+       "\n",
+       "     num_models  partial_fit_calls     score  std_fit_time  std_score_time  \\\n",
+       "201           5                 81  0.363630             0               0   \n",
+       "202           5                 81  0.251782             0               0   \n",
+       "203           5                 81  0.971782             0               0   \n",
+       "204           2                 81  0.703019             0               0   \n",
+       "205           2                 81  0.942910             0               0   \n",
+       "\n",
+       "     std_test_score std_train_score  \n",
+       "201               0            None  \n",
+       "202               0            None  \n",
+       "203               0            None  \n",
+       "204               0            None  \n",
+       "205               0            None  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.DataFrame(alg.history_)\n",
+    "print(df.bracket.max())\n",
+    "print(df.bracket_iter.unique())\n",
+    "df.tail()\n",
+    "# df[df.mean_test_score > 0.95]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.vegalite.v2+json": {
+       "$schema": "https://vega.github.io/schema/vega-lite/v2.4.3.json",
+       "config": {
+        "view": {
+         "height": 300,
+         "width": 400
+        }
+       },
+       "data": {
+        "values": [
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EAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAAGQIAIEMAAGQIAIAMAQCQIQAAMgQAQIYAAMgQAAAZAgAgQwAAZAgAgAwBAJAhAAAyBABAhgAAyBAAABkCACBDAABkCACADAEAkCEAADIEAECGAADIEAAA1QJVezU8cBZvIgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<VegaLite 2 object>\n",
+       "\n",
+       "If you see this message, it means the renderer has not been properly enabled\n",
+       "for the frontend that you are using. For more information, see\n",
+       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from altair import Chart\n",
+    "Chart(df).mark_circle().encode(\n",
+    "    x='bracket_iter', row='bracket', y='mean_test_score')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}