{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"\n",
    "    border: 2px solid #4CAF50; \n",
    "    padding: 15px; \n",
    "    background-color: #f4f4f4; \n",
    "    border-radius: 10px; \n",
    "    align-items: center;\">\n",
    "\n",
    "<h1 style=\"margin: 0; color: #4CAF50;\">ML Basics 2: Lernen von Daten</h1>\n",
    "<h2 style=\"margin: 5px 0; color: #555;\">DSAI</h2>\n",
    "<h3 style=\"margin: 5px 0; color: #555;\">Jakob Eggl</h3>\n",
    "\n",
    "<div style=\"flex-shrink: 0;\">\n",
    "    <img src=\"https://www.htl-grieskirchen.at/wp/wp-content/uploads/2022/11/logo_bildschirm-1024x503.png\" alt=\"Logo\" style=\"width: 250px; height: auto;\"/>\n",
    "</div>\n",
    "<p1> © 2024/25 Jakob Eggl. Nutzung oder Verbreitung nur mit ausdrücklicher Genehmigung des Autors.</p1>\n",
    "</div>\n",
    "<div style=\"flex: 1;\">\n",
    "</div>   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Beurteilung des Gelernten Models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im letzten Notebook haben wir gelernt, wie das Training von Modellen in etwa funktioniert und abschließend auch intuitive Beurteilungen von der Qualität des Models gemacht.\n",
    "\n",
    "Insbesondere haben wir diese Theorie immer anhand vom linearen Regressionsmodell erörtert. Obwohl für die verschiedenen Modelltypen dies unterschiedlich aussehen kann, stellt uns dieses Modell die Theorie sehr deutlich dar. \n",
    "\n",
    "![Underfitting_Regression](../resources/2_Underfitting_Wiederholung_Regression.png) vs. ![Overfitting_Regression](../resources/2_Overfitting_Wiederholung_Regression.png)\n",
    "\n",
    "(von https://visualize-it.github.io/polynomial_regression/simulation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun wollen wir das ganze formalisieren und im Detail besprechen.\n",
    "\n",
    "Dabei werden wir folgende Dinge besprechen (und mit Code durchführen):\n",
    "* **Overfitting und Underfitting**\n",
    "* **Train-Test Splits**\n",
    "* **Metriken zur Evaluierung (inkl. Confusion Matrix)**\n",
    "* **Tipps und Tricks**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overfitting und Underfitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die beiden obigen Bilder repräsentieren 2 wichtige Zustände, welche im Rahmen des Trainings eines Modells erreicht werden können: **Overfitting** und **Underfitting**.\n",
    "\n",
    "Beides sind **Zustände**, die wir **nicht erreichen wollen**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overfitting\n",
    "\n",
    "* Modell \"**lernt** die Trainingsdaten **auswendig**\"\n",
    "* Jeder Punkt wird genau getroffen\n",
    "* Für **neue Punkte** ist die Vorhersage **schlecht**\n",
    "* Beispiel: Stell dir vor, du lernst für eine Matheprüfung, indem du alle Aufgaben auswendig lernst, die im Buch stehen. Wenn in der Prüfung andere Aufgaben kommen, weißt du nicht, wie du sie lösen sollst."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Underfitting\n",
    "\n",
    "* Modell ist **zu einfach** um Muster aus den Daten zu erkennen\n",
    "* Fast kein Punkt wird genau getroffen\n",
    "* Kann **weder Trainingsdaten**, **noch** neue **Daten** gut **vorhersagen**\n",
    "* Beispiel: Stell dir vor, du lernst für eine Matheprüfung, indem du nur die Überschriften aus dem Mathe-Buch liest. Nun hast du zu wenig Wissen, die Aufgaben zu lösen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Der gute Mittelweg (Right Fit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir wollen ein Modell, welches:\n",
    "* die **Muster gut erkennt**\n",
    "* **kein** **\"auswendig lernen\"** der (Trainings)Daten\n",
    "* **Gute Vorhersagen** für **neue Daten** treffen kann"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Right Fit vs Overfitting and Underfitting](../resources/2_Overfitting_vs_Underfitting_Regression_Classification.png)\n",
    "\n",
    "(von https://de.mathworks.com/discovery/overfitting.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Polynomial Grad 3 Right Fit](../resources/2_Good_Tradeoff_Polynomial_Degree3.png)\n",
    "\n",
    "(von https://visualize-it.github.io/polynomial_regression/simulation.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wie können wir nun so ein Modell erhalten, bzw. wie können wir entscheiden, ob ein Modell zu einfach oder zu kompliziert ist?\n",
    "\n",
    "Um dies zu beantworten, sehen wir uns nun eine Technik an, bei der wir die Daten in sogenannte **Trainingsdaten** und **Testdaten** unterteilen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Train_Test_Split](../resources/2_Train_Test_Split.jpg)\n",
    "\n",
    "(von https://builtin.com/data-science/train-test-split)\n",
    "\n",
    "**Wichtig:** Auch bei den Testdaten besitzen wir das echte Label, hier mit $y$ gekennzeichnet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Was ist der Train-Test-Split?\n",
    "    * Methode um Datenset in **2 Teile** aufzuteilen\n",
    "        * **Trainingsdaten**: Werden vom Modell benutzt, um die Muster zu lernen.\n",
    "        * **Testdaten**: Werden verwendet, um zu überprüfen, ob das Modell auch bei neuen Daten gut funktioniert\n",
    "1. Warum brauchen wir das?\n",
    "    * Wir wollen sichergehen, dass das Modell nicht nur auswendig lernt (Overfitting) sondern auch gut verallgemeinern kann\n",
    "1. Wie teilt man die Daten auf?\n",
    "    * Frei wählbar, üblich sind aber etwa **80% Trainingsdaten** und **20% Testdaten**\n",
    "1. Beispiel:\n",
    "    * Stell dir vor, du lernst für eine Mathematikprüfung:\n",
    "        * Die Trainingsdaten sind einige Übungsbeispiele, bei denen auch die Lösung gegeben ist.\n",
    "        * Du versuchst nun mit Hilfe von den Lösungen die Aufgaben zu verstehen und zu lernen, wie man solche Beispiele löst\n",
    "        * Bei der Prüfung selber hast du aber keinen Zugang zu den Lösungen, weswegen du dir sicher sein willst, dass du die Beispiele tatsächlich verstanden hast und nicht nur die Übungsaufgaben auswendig gelernt hast\n",
    "        * Dafür verwendest du jetzt weitere Beispiele, von denen du die Lösung weißt (=Testdaten). Diese musst du nun lösen, ohne auf die Lösung zu schauen. Dafür kannst du im Nachhinein mit der Musterlösung vergleichen.\n",
    "1. Was gibt es sonst noch zu beachten?\n",
    "    * Die Testdaten dürfen nie im Trainingsdataset vorkommen! (Ansonsten könnte das Modell einfach auch die Testdaten auswendig lernen)\n",
    "    * Die Testdaten sollen das ganze Dataset gut repräsentieren, genauso die Trainingsdaten (**i.i.d.-Annahme** - siehe nächster Absatz)\n",
    "    * Es ist vernünftig (nicht nur hier, sondern allgemein), einen **seed** zu vergeben, somit bleiben die Ergebnisse reproduzierbar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Die i.i.d. Annahme"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eine Voraussetzung, um ein gutes Modell zu bekommen ist es, dass die Daten, die wir verwenden die **i.i.d.**-Annahme (**independent and identically distribueted** - also unabhängig und identisch verteilt) erfüllen. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Was bedeuten unabhängige (**independent**) Daten?\n",
    "    * Jeder Datenpnkt ist von den anderen Datenpunkten unabhängig.\n",
    "    * Das Ergebnis des einen Datenpunktes hängt nicht (mit dem Ergebnis) des nächsten Datenpunkts zusammen!\n",
    "    * Beispiel: Wenn du für die Matheprüfung lernst, dann soll jedes Beispiel eigenständig sein. Man will also nicht, dass du ein Beispiel nicht lernen kannst, weil du bereits ein anderes nicht gelernt hast und umgekehrt.\n",
    "1. Was bedeuten identisch verteilte (**identically distributed**) Daten?\n",
    "    * Alle Datenpunkte sind gleich wahrscheinlich\n",
    "    * Es soll damit garantiert werden, dass von jedem möglichen Bereich Daten vorhanden sind\n",
    "    * Beispiel: Wenn du für die Matheprüfung lernst, dann wäre es schlecht, wenn in den Übungsbeispielen ein Kapitel nicht vorhanden ist. Dieses würdest du dann nicht lernen können und wärst dann schlecht für den Test vorbereitet."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sollten die Daten (und von dem gehen wir normalerweise aus) die **i.i.d.** Annahme erfüllen, so darf zum Beispiel das Mischen der Datenpunkte (Reihenfolge in dem Dataset) keinen Unterschied machen!\n",
    "* **Achtung:** Bei Sequentiellen Daten (zum Beispiel Audio, Text oder Zeitreihen im Allgemeinen) muss man aufpassen, hier darf man die Daten natürlich nicht einfach auf dieser Ebene mischen? Erfüllen Sie trotzdem die **i.i.d.** Voraussetzung?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **Wichtig** ist auch: In beiden Datasets (Train und Test) sollen in etwa die gleichen \"Arten von Daten\" vorkommen:\n",
    "    * Im vorigen Beispiel (Lernen für die Mathematik Prüfung) heißt das konkret: Es sollen in beiden Aufgabensets (Training und Test) alle Arten von Problemen auftauchen. Es macht wenig Sinn, wenn zum Beispiel in den Übungsaufgaben nur Aufgaben zum Thema Differenzieren und im Testbeispiel nur Beispiele zum Thema Integrieren vorkommen!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train/Test Split in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets import load_iris, load_wine\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zuerst Laden wir das Dataset. Bisher haben wir nur eine Variable bekommen, diesmal wollen wir direkt x und y!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = load_iris(return_X_y=True, as_frame=False) # Wir verwenden as_frame = False (=Default), damit wir nur die Daten bekommen (als Array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "       [4.6, 3.1, 1.5, 0.2],\n",
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       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
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       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
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       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
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       "       [5.4, 3.4, 1.7, 0.2],\n",
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       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
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       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [4.7, 3.2, 1.6, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [5.4, 3.4, 1.5, 0.4],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
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       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.8, 3. , 1.4, 0.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
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       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
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       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.6, 2.5, 3.9, 1.1],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.1, 2.8, 4.7, 1.2],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [7.7, 2.6, 6.9, 2.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [6.4, 2.8, 5.6, 2.1],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.7, 3. , 5.2, 2.3],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.9, 3. , 5.1, 1.8]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 4)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150,)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wir splitten die Daten in Trainings- und Testdaten\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # mischt das Dataset automatisch. Ansonsten hätten wir viel weniger mit Label 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4.6, 3.6, 1. , 0.2],\n",
       "       [5.7, 4.4, 1.5, 0.4],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [4.8, 3.4, 1.6, 0.2],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [6.3, 2.5, 5. , 1.9],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [5.2, 4.1, 1.5, 0.1],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [5.5, 2.4, 3.7, 1. ],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [6.6, 3. , 4.4, 1.4],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.1, 3.5, 1.4, 0.2],\n",
       "       [7.7, 2.8, 6.7, 2. ],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [5.2, 3.4, 1.4, 0.2],\n",
       "       [5. , 3.5, 1.3, 0.3],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [6.3, 2.7, 4.9, 1.8],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [6.2, 2.9, 4.3, 1.3],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [6. , 2.2, 4. , 1. ],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [6.7, 3. , 5. , 1.7],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [6.9, 3.2, 5.7, 2.3],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [7.7, 3. , 6.1, 2.3],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [4.4, 2.9, 1.4, 0.2],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [7.2, 3.2, 6. , 1.8],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [6.8, 3. , 5.5, 2.1],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.4, 3.2, 5.3, 2.3],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [4.9, 3. , 1.4, 0.2],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6.5, 3. , 5.5, 1.8],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [5.9, 3. , 5.1, 1.8],\n",
       "       [5.4, 3.4, 1.7, 0.2],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [7.1, 3. , 5.9, 2.1]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun stellt sich nach wie vor die Frage, wie wir nun bewerten können, ob unser Modell gut performt.\n",
    "\n",
    "Dies ist, wie bereits besprochen, für eine Regression ziemlich einfach. Für kompliziertere Modelle (und für eine objektive Beurteilung von einfachen Modellen) führen wir jetzt ein paar Metriken ein, welche diese Performance messen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Metriken zur Evaluierung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun besprechen wir verschiedene **Metriken**, welche verwendet werden können um die **Perfomance** von **Regressions- und Klassifikationsmodellen** zu **bewerten**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressionsmetriken"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Die üblichste Möglichkeit, ein Regressionsmodell zu beurteilen ist es, den **Mean Squared Error** (*MSE*) zu berechnen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Evaluation_Regression](../resources/2_Regression_Evaluation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dazu nehmen wir das trainierte Modell (nur auf den Trainingsdaten trainiert) und berechnen für jeden Punkt in den Testdaten die quadratische Abweichung zum Wert, der vorhergesagt wurde. Abschließend berechnen wir noch den Mittelwert dieser Abweichung.\n",
    "\n",
    "Die Formel ist also\n",
    "\n",
    "\\begin{equation*}\n",
    "    \\text{MSE} = \\frac{1}{n}\\sum_{i=1}^{n}(y_{\\text{true}}-y_{\\text{predicted}})^2\n",
    "\\end{equation*}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idealerweise ist dieser Fehler natürlich klein!\n",
    "\n",
    "**Wichtig:** Es darf kein Testpunkt in den Trainingsdaten sein und umgekehrt. Ansonsten ist der berechnete Wert ohne Aussagekraft!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Metriken für die Klassifizierung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Im Fall von 2 Klassen erstellt unser Modell also für jeden Datenpunkt einen Output (Vorhersage) von entweder $0$ oder $1$. In unserem Testset haben wir das echte (true) Label, somit können wir einfach zählen, wie oft die Prediciton von unserem Modell mit jener vom echten Dataset zusammen stimmt. Dies ist eine Möglichkeit zur Evaluierung und nennt sich **Accuracy**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "\\text{Accuracy} = \\frac{\\text{Anzahl der korrekten Vorhersagen}}{\\text{Anzahl der Datenpunkte im Testset}}.\n",
    "\\end{equation*} \n",
    "\n",
    "Ein perfekter Klassifizierer hat also eine Accuracy von $1$, also $100\\%$ richtige Vorhersagen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es gibt auch noch die **Balanced Accuracy**, welche die Klassenverteilung miteinbezieht, diese wird aber jetzt nicht mathematisch Beschrieben. Anwendbar ist sie in Python sowieso ziemlich einfach via `scikit-learn`!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Es gibt aber noch weitere Metriken, welche für Klassifizierung verwendet werden können.\n",
    "\n",
    "Ein Grund für die große Auswahl ist, weil man je nach **Anwendungsfall** auf **andere Eigenschaften** des Modells Wert legt!\n",
    "\n",
    "Dafür betrachten wir nun zuerst die sogenannte **Confusion Matrix**!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eine **Confusion Matrix** zeigt die Performance eines Klassifikationsmodells, indem sie echte und vorhergesagte Klassen vergleicht.  \n",
    "Beispiel für eine 2-Klassen-Klassifikation:  \n",
    "\n",
    "|                  | **Positive Vorhersage** | **Negative Vorhersage** |\n",
    "|------------------|--------------------------|--------------------------|\n",
    "| **Positive Fälle** | **True Positive (TP)**  | **False Negative (FN)**  |\n",
    "| **Negative Fälle** | **False Positive (FP)** | **True Negative (TN)**   |\n",
    "\n",
    "- **True Positive (TP):** Modell sagt \"positiv\" und es ist korrekt.  \n",
    "- **False Positive (FP):** Modell sagt \"positiv\", aber es ist falsch.  \n",
    "- **True Negative (TN):** Modell sagt \"negativ\" und es ist korrekt.  \n",
    "- **False Negative (FN):** Modell sagt \"negativ\", aber es ist falsch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In diesem Fall bedeuten positiv und negativ zum Beispiel Klasse $1$ und Klasse $0$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mit diesen können wir nun **weitere Metriken** zur Bewertung von Klassifikationsmodellen definieren!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}.\n",
    "\\end{equation*} \n",
    "\n",
    "Die **Precision** gewichtet Falsch-Positive (FP) Fälle auch mit ein. Dieser Wert soll hoch sein, wenn wir Falsch-Positive Vorhersagen problematisch sind (zum Beispiel bei Krankheitstest (Corona))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}.\n",
    "\\end{equation*} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Der **Recall** wird auch **True Positive Rate** (*TPR*) genannt. Dementsprechend gibt es auch eine **True Negative Rate** (*TNR*). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\\begin{equation*}\n",
    "\\text{TNR} = \\frac{\\text{TN}}{\\text{TN} + \\text{FP}}.\n",
    "\\end{equation*} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Eine weitere Metrik ist der sogenannte $F1$-Score:\n",
    "\\begin{equation*}\n",
    "\\text{F1-Score} = 2\\cdot \\frac{\\text{Precision}\\cdot \\text{Recall}}{\\text{Precision} + \\text{Recall}}.\n",
    "\\end{equation*} "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Wichtig:** Die vorigen Metriken funktionieren nur für die binäre Klassifikation (sprich $0$ oder $1$ Klassifizierung). Für Multiclass Klassifizierung verwendet man deswegen hauptsächlich die Accuracy oder ändert die genannten Metriken leicht ab!\n",
    "\n",
    "Dies gilt natürlich auch für die Confusion Matrix im Allgemeinen!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Confusion Matrix für Multiclass Klassifikation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Multiclass Confusion Matrix](../resources/2_Confusion_Matrix_Multiclass.png)\n",
    "\n",
    "(from AI Vis 2024WS Prof. Marc Streit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nun muss bei einer Falschklassifikation unterschieden werden, welche Klasse anstelle der richtigen Klasse vorhergesagt wurde!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluierung in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Insgesamt bietet auch hier `scikit-learn` viele praktische Funktionen, sodass sehr wenig ausprogrammiert werden muss."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir werden nun unser erstes Modell trainieren!🥳\n",
    "\n",
    "Wir betrachten jetzt nur Klassifikation. Außerdem geht es noch nicht um das Modell selber, sondern um die Evaluierung im Anschluss.\n",
    "\n",
    "Das Trainieren der Modelle passiert nächste Woche!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier # Laden der Modellklasse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Wir verenden das wine dataset\n",
    "X, y = load_wine(return_X_y=True, as_frame=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178, 13)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(178,)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "number_of_neighbors = 7\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors=number_of_neighbors) # Instanziieren des Modells\n",
    "trained_model = knn.fit(X_train, y_train) # Trainieren des Modells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model Evaluierung\n",
    "\n",
    "y_pred = trained_model.predict(X_test) # Vorhersage auf den Testdaten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 0, 1, 0, 2, 0, 2, 0, 2, 2, 0, 1, 0, 1, 1, 2, 0, 1, 0, 1,\n",
       "       2, 1, 1, 1, 1, 2, 1, 0, 0, 1, 2, 0, 0, 0])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 2, 0, 1, 0, 1, 2, 1, 2, 0, 2, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1,\n",
       "       1, 2, 2, 2, 1, 1, 1, 0, 0, 1, 2, 0, 0, 0])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "conf_matrix = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die Accuracy beträgt: 0.6944444444444444\n"
     ]
    }
   ],
   "source": [
    "print(f\"Die Accuracy beträgt: {accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Die Confusion Matrix lautet:\n",
      "[[13  0  1]\n",
      " [ 0  9  5]\n",
      " [ 2  3  3]]\n"
     ]
    }
   ],
   "source": [
    "print(f\"Die Confusion Matrix lautet:\\n{conf_matrix}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wir können nun auch die Confusion Matrix mit einer Heatmap visualisieren!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(conf_matrix, annot=True, cmap='Blues')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vielleicht schafft ein anderes Modell (oder andere Hyperparameter) eine bessere Performance?\n",
    "\n",
    "Wie sieht es mit den Iris Daten aus?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tipps and Tricks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Falls ein Modell schlecht performt, kann man sich folgende Dinge überlegen:\n",
    "* Daten normalisieren (Beispiel k-nearest-neighbor)\n",
    "* Mehr Daten beschaffen (eventuell Data Augmentation möglich)\n",
    "* **Model kleiner** machen, indem manche Features nicht verwendet werden (hilft auch oft gegen Overfitting), zum Beispiel mittels Korrelationsmatrix, um zu sehen, welche Features mehr oder weniger das gleiche Verhalten haben.\n",
    "\n",
    "Letzteren Punkt sehen wir uns jetzt nochmal mit Python an!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation Matrix\n",
    "\n",
    "correlation_matrix = np.corrcoef(X_train, rowvar=False) # Berechnen den Korrelationskoeffizienten (wie in den Visualisierungsunterlagen besprochen)\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Hohe Werte können nun entfernt werden und das Model wird kleiner, somit auch schneller mit vergleichsweise wenig (oder keinem) Verlust in der Performance. Oft verbessert das die Perfomance am Testset sogar (Overfitting)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aufgabe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Verwende das Dataset, welches für die bisherigen Gruppenarbeiten bereits verwendet wurde und schaue dir die Größe an\n",
    "* Versuche das Dataset mit Hilfe von `sklearn` in einen Train- und in einen Testteil zu unterteilen.\n",
    "* Funktioniert es auch mit Dataframes?\n",
    "* Wie kann ich aus einem bestehenden Dataframe das Label extrahieren?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dsai",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}