194 lines
4.2 KiB
Plaintext
194 lines
4.2 KiB
Plaintext
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{
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"# WS 13 Kreuzvalidierung"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"* vergleichen Sie alle bisher bekannten Klassifikatoren (ausser SVC und MLPClassifier) in Bezug auf deren Stabilität unter Anwendung von Kreuzvalidierung\n",
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"* verwenden Sie für die Klassifikatoren jeweils Default-Parametrisierung\n",
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"* setzen Sie für die Kreuzvalidierung folgende Funktion ein: `sklearn.model_selection.cross_val_score`"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"## load libraries\n",
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"import pandas as pd\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import seaborn as sns; sns.set()\n",
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"\n",
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"## load data\n",
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"datapath = '../3_data'\n",
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"from os import chdir; chdir(datapath)\n",
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"bank_df = pd.read_csv('bank_data_prep.csv')\n",
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"\n",
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"## features - target - tplit\n",
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"X = bank_df.drop('y', axis=1)\n",
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"y = bank_df['y']\n",
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"\n",
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"## train - test - split\n",
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"## obsolete here, is done internally by cross validation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"KNeighborsClassifier\n",
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"DecisionTreeClassifier\n",
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"RandomForestClassifier\n"
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]
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}
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],
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"source": [
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"from sklearn.neighbors import KNeighborsClassifier\n",
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"from sklearn.tree import DecisionTreeClassifier\n",
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"from sklearn.ensemble import RandomForestClassifier\n",
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"## tbd complete\n",
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"\n",
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"\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"from sklearn.model_selection import cross_val_score\n",
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"\n",
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"models = [\n",
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" KNeighborsClassifier(),\n",
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" DecisionTreeClassifier(),\n",
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" RandomForestClassifier()\n",
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" ## tbd complete\n",
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" \n",
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" \n",
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"]\n",
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"\n",
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"kfold = 5\n",
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"model_names = []\n",
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"model_scores = []\n",
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"\n",
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"for model in models:\n",
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" model_name = model.__class__.__name__\n",
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" print(model_name)\n",
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" ## tbd\n",
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"\n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"## manage results, e.g. in pandas dataframe\n",
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"## tbd\n",
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"\n",
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"\n",
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"\n",
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"## visualize results\n",
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"## tbd\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"**Fazit:**\n",
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"* tbd"
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]
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}
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],
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"title_cell": "WS 16 Validierung - Kreuzvalidierung",
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