cas-pml/SL/aufgaben/template/4_WS/WS 07 Vorlage.ipynb

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    "<h1>WS 10 Klassifikation - Modellvergleiche<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"></ul></div>"
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    "* vergleichen Sie alle bis jetzt vorgestellten Klassifikatoren miteinander in Bezug auf\n",
    "  * Performance\n",
    "  * Rechenzeiten, differenziert nach .fit() und .predict()  \n",
    "  und visualisieren Sie die Ergebnisse\n",
    "* Tipp: modifizieren / ergänzen Sie dazu den abgegebenen Code von Kapitel 2.2.6 Modellvergleiche\n",
    "\n",
    "* optional: fügen Sie andere, im Kurs nicht behandelte Klassifikatoren dazu, welche Sie in der Dokumentation von scikit-learn finden\n",
    "* optional: falls Sie im Rahmen von Feaure Engineering alternatives Preprocessing erarbeitet haben, können Sie die Auswirkungen desselben jetzt auch noch einbeziehen\n",
    "* optional: wie wirkt sich Skalierung (z.B. mit StandardScaler) auf die Performance von MLPClassifier aus?"
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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; 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)"
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    "## Funktionen (Klassen) importieren\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "## tbd ergänzen\n",
    "\n",
    "\n",
    "\n",
    "from sklearn.metrics import accuracy_score\n",
    "import time  ## für Zeitmessung"
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    "## Modelle definieren und in Liste hinterlegen\n",
    "models = [\n",
    "    KNeighborsClassifier(),\n",
    "    DecisionTreeClassifier(min_impurity_decrease=0.002),\n",
    "    RandomForestClassifier(n_estimators=100)\n",
    "    ## tbd ergänzen\n",
    "    \n",
    "    \n",
    "    \n",
    "]"
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      "KNeighborsClassifier()\n",
      "DecisionTreeClassifier(min_impurity_decrease=0.002)\n",
      "RandomForestClassifier()\n"
     ]
    }
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   "source": [
    "## zum Sammeln der Resultate\n",
    "scores = []\n",
    "times_fit = []\n",
    "times_pred = []\n",
    "model_names = []\n",
    "\n",
    "#print('Classifier                          Score      Time fit    Time pred')\n",
    "#print('====================================================================')\n",
    "\n",
    "## Loop\n",
    "for model in models:\n",
    "    print(model)\n",
    "    ## tbd\n",
    "    \n",
    "    ##   start timer1 - fit - stop timer1\n",
    "    \n",
    "    \n",
    "    ##   start timer2 - predict - stop timer2\n",
    "    \n",
    "    \n",
    "    ##   berechne Score & pick Modellname\n",
    "    \n",
    "    \n",
    "    ##   Ergebnisse an vorbereitete Listen anhängen\n",
    "    \n",
    "    \n",
    "    ##   Iterationsergebnisse in Konsole ausgeben (optional)\n",
    "\n",
    "    "
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
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   "source": [
    "## visualisieren\n",
    "## tbd ergänzen\n",
    "\n",
    "\n"
   ]
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   "source": [
    "**Fazit:**  \n",
    "* tbd\n",
    "\n",
    "\n"
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