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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "8459055e",
+   "metadata": {},
+   "source": [
+    "# Classification\n",
+    "\n",
+    "**_NOTE_** autosklearn only will run in linux (feb 26, 2022)\n",
+    "\n",
+    "Example coming from [here](https://automl.github.io/auto-sklearn/master/examples/20_basic/example_classification.html#sphx-glr-examples-20-basic-example-classification-py)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c69433ce",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "from pprint import pprint\n",
+    "\n",
+    "import sklearn.datasets\n",
+    "import sklearn.metrics\n",
+    "import pickle\n",
+    "\n",
+    "import autosklearn.classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2b1e1930",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# split the dataset\n",
+    "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n",
+    "X_train, X_test, y_train, y_test = \\\n",
+    "    sklearn.model_selection.train_test_split(X, y, random_state=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "15e5f821",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Fit the classifier\n",
+    "automl = autosklearn.classification.AutoSklearnClassifier(\n",
+    "    time_left_for_this_task=120,\n",
+    "    per_run_time_limit=30,\n",
+    "    tmp_folder='/tmp/autosklearn_classification_example_tmp',\n",
+    ")\n",
+    "automl.fit(X_train, y_train, dataset_name='breast_cancer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2d4e4d9f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Different Models run by autosklearn\n",
+    "print(automl.leaderboard())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "72e580e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Show the different models\n",
+    "pprint(automl.show_models(), indent=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "027039cd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Predict the test labels\n",
+    "predictions = automl.predict(X_test)\n",
+    "print(\"Accuracy score:\", sklearn.metrics.accuracy_score(y_test, predictions))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "acd372ea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Export the model with the highest rank\n",
+    "clf = automl.show_models()[7]['sklearn_classifier']\n",
+    "pickle.dump(clf,open('model.pickle','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3324782",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "021b7159",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}