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    "# WS 05 Klassifikation - DecisionTreeClassifier  "
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    "* untersuchen Sie verschiedene Werte von min_impurity_decrease bei DecisionTreeClassifier auf die erreichbare Performance (Accuracy)\n",
    "* grenzen Sie dabei den zu untersuchenden Wertebereich schrittweise ein\n",
    "* stellen Sie dazu die Ergebnisse wie folgt dar\n",
    "  * grafisch als Liniendiagramm\n",
    "  * in der Konsole mit bestem Score und entsprechendem Parameterwert\n",
    "* Hinweis\n",
    "  * `range()`: erstellt einen Bereich von Ganzzahligen Werten mit identischer Schrittweite\n",
    "  * `np.arange()`: (Funktion von numpy) erstellt mit analoger Parametrisierung einen Bereich mit Gleitkommawerten"
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    "## prepare env, read and prepare data\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set()\n",
    "\n",
    "codepath = '../2_code'  ## for import of user defined module\n",
    "datapath = '../3_data'\n",
    "\n",
    "from sys import path\n",
    "path.insert(1, codepath)\n",
    "\n",
    "from os import chdir\n",
    "chdir(datapath)\n",
    "\n",
    "from bfh_cas_pml import prep_data\n",
    "\n",
    "X_train, X_test, y_train, y_test = prep_data('bank_data_prep.csv',\n",
    "                                                  target='y',\n",
    "                                                  seed=1234)"
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      "0.0\n",
      "0.01\n",
      "0.02\n",
      "0.03\n",
      "0.04\n",
      "0.05\n",
      "0.06\n",
      "0.07\n",
      "0.08\n",
      "0.09\n"
     ]
    }
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   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "model = DecisionTreeClassifier()\n",
    "\n",
    "scores = []\n",
    "params = np.arange(0, 0.1, 0.01)\n",
    "\n",
    "for param in params:\n",
    "    print(param)\n",
    "    ## tbd\n",
    "    \n",
    "\n",
    "    "
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