Teil2_Vorlage.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Übung\n",
    "### Ziel: Einführung in die gängigsten Bewertungsmetriken und erste Hands-On Erfahrungen sammeln\n",
    "### Datensatz: Banking-Datensatz - aufbereitet und bereitgestellt von SciKit-Learn"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Importiere folgende Packages:\n",
    "- pandas "
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier importieren"
   ]
  },
  {
   "source": [
    "Lade Daten"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Datensatz laden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gebe den Datensatz aus"
   ]
  },
  {
   "source": [
    "# Bewertungskriterien\n",
    "1. Treshold Metrics\n",
    "2. Ranking Metrics\n",
    "3. Probability Metrics"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# 1. Threshold Metrics \n",
    "\n",
    "Treshold Metrics sind solche, die die Klassifikationsvorhersagefehler quantifizieren.\n",
    "\n",
    "\n",
    "- Precision\n",
    "- Recall\n",
    "- F1 Score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Übung: Berechne die Precision\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn.metrics.precision_score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Precision für Modell1 berechnen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Precision für Modell2 berechnen"
   ]
  },
  {
   "source": [
    "Übung: Berechne den Recall\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.recall_score.html#sklearn.metrics.recall_score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Recall für Modell1 berechnen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Recall für Modell2 berechnen"
   ]
  },
  {
   "source": [
    "Übung: Berechne den F1 Score\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "# Hier F1 Score für Modell1 berechnen"
   ],
   "cell_type": "code",
   "metadata": {},
   "execution_count": 33,
   "outputs": []
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier F1 Score für Modell2 berechnen"
   ]
  },
  {
   "source": [
    "# 2. Ranking Metrics\n",
    "\n",
    "Ranking Metrics befassen sich mehr mit der Bewertung von Klassifikatoren basierend darauf, wie effektiv sie beim Trennen von Klassen sind."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Übung: Berechne und stelle den ROC dar\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_curve.html?highlight=roc%20curve#sklearn.metrics.roc_curve"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Berechne hier zunächst den ROC Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stelle hier die ROC Curve dar"
   ]
  },
  {
   "source": [
    "# 3. Probabilistic Metrics\n",
    "\n",
    "Probabilistic Metrics sind speziell darauf ausgelegt, die Unsicherheit in den Vorhersagen eines Klassifikators zu quantifizieren."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "Übung: Berechne den Brier Score\n",
    "\n",
    "https://scikit-learn.org/stable/modules/generated/sklearn.metrics.brier_score_loss.html#sklearn.metrics.brier_score_loss"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Brier Score für Modell1 berechnen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hier Brier Score für Modell2 berechnen"
   ]
  }
 ]
}