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