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    "# WS 06 Klassifikation - RandomForestClassifier"
   ]
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   "source": [
    "* untersuchen Sie die folgenden Tuning-Parameter von RandomForestClassifier in Bezug auf die erreichte Performance (accuracy_score) mit dem vorbereiteten Dataset:\n",
    "  * n_estimators als `range(100, 500, 50)`\n",
    "  * max_features als `range(1, 11)`\n",
    "  * min_impurity_decrease als `np.arange(0, 0.1, 0.01)`\n",
    "* wie wirkt sich der random_state aus?\n",
    "* welche der ausserdem zur Verfügung stehenden Parameter sind keine Tuning Parameter? Konsultieren Sie dazu die (Online-) Dokumentation"
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 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; sns.set()\n",
    "\n",
    "codepath = '../2_code' ## for import of user defined module\n",
    "datapath = '../3_data'\n",
    "from sys import path; path.insert(1, codepath)\n",
    "from os import chdir; chdir(datapath)\n",
    "\n",
    "from bfh_cas_pml import prep_data\n",
    "X_train, X_test, y_train, y_test = prep_data('bank_data_prep.csv', target='y', seed=1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "n_estimators:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "150\n",
      "200\n",
      "250\n",
      "300\n",
      "350\n",
      "400\n",
      "450\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "scores = []\n",
    "params = range(100, 500, 50)\n",
    "\n",
    "for param in params:\n",
    "    print(param)\n",
    "    ## tbd\n",
    "    \n",
    "\n",
    "## tbd\n",
    "#fig = sns.lineplot(x=params, y=scores)\n",
    "#...\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "max_features:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "scores = []\n",
    "params = range(1, 11)\n",
    "\n",
    "for param in params:\n",
    "    print(param)\n",
    "    ## tbd\n",
    "    \n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_impurity_decrease:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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"
     ]
    }
   ],
   "source": [
    "model = RandomForestClassifier()\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",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fazit:**\n",
    "* tbd\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "keine Tuning Parameter sind hier:\n",
    "* tbd\n",
    "\n",
    "\n"
   ]
  }
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