kap2.ipynb

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    "language_info": {
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    },
    "colab": {
      "name": "kap2.ipynb",
      "provenance": [],
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/DJCordhose/buch-machine-learning-notebooks/blob/master/kap2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yJ2zzYGxh5R4",
        "colab_type": "text"
      },
      "source": [
        "# Kapitel 2: Einführung in Machine Learning - Schnelldurchgang"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "iRA-vO-4h5R4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "k6hArp6Xh5R8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6fe188ed-dacb-45c4-e03e-a27839145cc0"
      },
      "source": [
        "%matplotlib inline\n",
        "%pylab inline"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Populating the interactive namespace from numpy and matplotlib\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "hEj2QxI7h5R_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pylab as plt\n",
        "import numpy as np"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "cz-aoJ07h5SE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "9ac8c458-aca3-4b38-96c0-b495c1ab7375"
      },
      "source": [
        "import sklearn\n",
        "print(sklearn.__version__)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.22.2.post1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xd9-n2YPh5SH",
        "colab_type": "text"
      },
      "source": [
        "# Zuerst laden wir den Iris Datensatz und verschaffen uns einen ersten Eindruck\n",
        "https://de.wikipedia.org/wiki/Portal:Statistik/Datensaetze#Iris"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "kIgspd1Eh5SH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.datasets import load_iris\n",
        "iris = load_iris()"
      ],
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "hkI42E3Rh5SJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "03468b39-5fea-4e99-ff1d-c874b113e648"
      },
      "source": [
        "print(iris.DESCR)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            ".. _iris_dataset:\n",
            "\n",
            "Iris plants dataset\n",
            "--------------------\n",
            "\n",
            "**Data Set Characteristics:**\n",
            "\n",
            "    :Number of Instances: 150 (50 in each of three classes)\n",
            "    :Number of Attributes: 4 numeric, predictive attributes and the class\n",
            "    :Attribute Information:\n",
            "        - sepal length in cm\n",
            "        - sepal width in cm\n",
            "        - petal length in cm\n",
            "        - petal width in cm\n",
            "        - class:\n",
            "                - Iris-Setosa\n",
            "                - Iris-Versicolour\n",
            "                - Iris-Virginica\n",
            "                \n",
            "    :Summary Statistics:\n",
            "\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "                    Min  Max   Mean    SD   Class Correlation\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "    sepal length:   4.3  7.9   5.84   0.83    0.7826\n",
            "    sepal width:    2.0  4.4   3.05   0.43   -0.4194\n",
            "    petal length:   1.0  6.9   3.76   1.76    0.9490  (high!)\n",
            "    petal width:    0.1  2.5   1.20   0.76    0.9565  (high!)\n",
            "    ============== ==== ==== ======= ===== ====================\n",
            "\n",
            "    :Missing Attribute Values: None\n",
            "    :Class Distribution: 33.3% for each of 3 classes.\n",
            "    :Creator: R.A. Fisher\n",
            "    :Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)\n",
            "    :Date: July, 1988\n",
            "\n",
            "The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
            "from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
            "Machine Learning Repository, which has two wrong data points.\n",
            "\n",
            "This is perhaps the best known database to be found in the\n",
            "pattern recognition literature.  Fisher's paper is a classic in the field and\n",
            "is referenced frequently to this day.  (See Duda & Hart, for example.)  The\n",
            "data set contains 3 classes of 50 instances each, where each class refers to a\n",
            "type of iris plant.  One class is linearly separable from the other 2; the\n",
            "latter are NOT linearly separable from each other.\n",
            "\n",
            ".. topic:: References\n",
            "\n",
            "   - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
            "     Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
            "     Mathematical Statistics\" (John Wiley, NY, 1950).\n",
            "   - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
            "     (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.\n",
            "   - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
            "     Structure and Classification Rule for Recognition in Partially Exposed\n",
            "     Environments\".  IEEE Transactions on Pattern Analysis and Machine\n",
            "     Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
            "   - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\".  IEEE Transactions\n",
            "     on Information Theory, May 1972, 431-433.\n",
            "   - See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al\"s AUTOCLASS II\n",
            "     conceptual clustering system finds 3 classes in the data.\n",
            "   - Many, many more ...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "1z2taSz2h5SM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X = iris.data\n",
        "y = iris.target"
      ],
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "Gm188qjuh5SO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e69240d2-17f1-43e3-cfcc-827a5d8bdc8d"
      },
      "source": [
        "X.shape, y.shape"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((150, 4), (150,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "_8xzbSf7h5SQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b18ff0a7-94a8-4288-e198-85e67b39e507"
      },
      "source": [
        "X[0]"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([5.1, 3.5, 1.4, 0.2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "n9Tp4ChZh5ST",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "d639db72-a3c2-40d0-aa4b-363e3807a98e"
      },
      "source": [
        "y[0]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "riwRv79Lh5SV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_sepal_length = X[:, 0]\n",
        "X_sepal_width =  X[:, 1]\n",
        "X_petal_length = X[:, 2]\n",
        "X_petal_width = X[:, 3]"
      ],
      "execution_count": 97,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "6gnLtuwih5SX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "03956c30-7180-4e63-a25e-de5f92c55422"
      },
      "source": [
        "X_petal_width.shape"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RAZnNuCph5SZ",
        "colab_type": "text"
      },
      "source": [
        "## Aufteilung der Daten in Training (60%) und Test (40%)\n",
        "http://scikit-learn.org/stable/modules/cross_validation.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "JNfhRsgAh5Sa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 99,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "p03uUASgh5Sc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=42, stratify=y)"
      ],
      "execution_count": 100,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "xv85Mo2hh5Se",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a9c94559-5de1-43cb-b687-7bb02f00eae3"
      },
      "source": [
        "X_train.shape, y_train.shape, X_test.shape, y_test.shape"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((90, 4), (90,), (60, 4), (60,))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JDhcs5cvh5Sg",
        "colab_type": "text"
      },
      "source": [
        "## Wir trainieren einen einfachen KNN Klassifikator und überprüfen die Ergebnisse\n",
        "http://scikit-learn.org/stable/modules/neighbors.html#classification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "zajQJRuLh5Sh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn import neighbors"
      ],
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "ooWLPyy1h5Sj",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "clf = neighbors.KNeighborsClassifier(1)"
      ],
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "uDPr67RIh5Sl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "a60e8276-3a87-4823-ce4a-e789ca7d53d6"
      },
      "source": [
        "clf.fit(X_train, y_train)"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "Sjtvakekh5Sn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sample_id = 32\n",
        "sample_feature = X_test[sample_id]\n",
        "sample_label = y_test[sample_id]"
      ],
      "execution_count": 105,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "XAmyuqa5h5Sq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "edfe666a-b9d1-4ad1-f879-f8df79cc1233"
      },
      "source": [
        "sample_feature"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([6.1, 2.6, 5.6, 1.4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "Cy8QQXBQh5Ss",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0303947f-dd8f-42eb-e57e-89fc2f7bc943"
      },
      "source": [
        "sample_label"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "wcJF-Qw8h5Sw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "dcffc738-7caf-41aa-a00e-2d3bb717c29c"
      },
      "source": [
        "clf.predict([sample_feature])"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "4KVLaOCmh5Sy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2dc7adb1-3457-4892-d4f5-8feb205e2d0e"
      },
      "source": [
        "clf.predict([[6.3, 2.7, 5.5, 1.5]]) # slightly different from above, still gives 2"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "bt934zU9h5S1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf904daa-8c0f-4df2-f946-a985c9ab9110"
      },
      "source": [
        "clf.score(X_train, y_train)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "8-_Swmw0h5S4",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cf23117d-c05e-4716-ac0e-6296d1510208"
      },
      "source": [
        "clf.score(X_test, y_test)"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.95"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3GjVZmfCh5S5",
        "colab_type": "text"
      },
      "source": [
        "## Um zu versehen, was durch das Training passiert ist, zeichnen wir die Decision Boundaries ein"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "rZAypnGch5S6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ignore this, it is just technical code\n",
        "# should come from a lib, consider it to appear magically \n",
        "# http://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.colors import ListedColormap\n",
        "\n",
        "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
        "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
        "font_size=25\n",
        "\n",
        "def meshGrid(x_data, y_data):\n",
        "    h = .02  # step size in the mesh\n",
        "    x_min, x_max = x_data.min() - 1, x_data.max() + 1\n",
        "    y_min, y_max = y_data.min() - 1, y_data.max() + 1\n",
        "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
        "                         np.arange(y_min, y_max, h))\n",
        "    return (xx,yy)\n",
        "    \n",
        "def plotPrediction(clf, x_data, y_data, x_label, y_label, colors, title=\"\", mesh=True):\n",
        "    xx,yy = meshGrid(x_data, y_data)\n",
        "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
        "\n",
        "    # Put the result into a color plot\n",
        "    Z = Z.reshape(xx.shape)\n",
        "    plt.figure(figsize=(20,10))\n",
        "    if mesh:\n",
        "        plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
        "    plt.xlim(xx.min(), xx.max())\n",
        "    plt.ylim(yy.min(), yy.max())\n",
        "    plt.scatter(x_data, y_data, c=colors, cmap=cmap_bold, s=80, marker='o')\n",
        "    plt.xlabel(x_label, fontsize=font_size)\n",
        "    plt.ylabel(y_label, fontsize=font_size)\n",
        "    plt.title(title, fontsize=font_size)"
      ],
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NSO-j7Ezh5S8",
        "colab_type": "text"
      },
      "source": [
        "### Zuerst für die Sepal Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "oXUD3kUWh5S8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_sepal_only = X_train[:, :2]\n",
        "X_test_sepal_only = X_test[:, :2]"
      ],
      "execution_count": 113,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "-0i95Fawh5S-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "c2895eaf-05ea-4c3f-bf74-43d6660a1e48"
      },
      "source": [
        "clf_sepal = neighbors.KNeighborsClassifier(1)\n",
        "clf_sepal.fit(X_train_sepal_only, y_train)"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=1, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "5Y9Kt79Dh5TA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "3b70c232-0833-4aa2-c1fe-f98073f58cae"
      },
      "source": [
        "plotPrediction(clf_sepal, X_train_sepal_only[:, 0], X_train_sepal_only[:, 1], \n",
        "               'Sepal length', 'Sepal width', y_train, mesh=False,\n",
        "                title=\"Train Data for Sepal Features\")\n",
        "# plt.savefig('ML_0201.png', bbox_inches='tight')"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RjqDi_1Rh5TC",
        "colab_type": "text"
      },
      "source": [
        "### Scores sind gut für die Trainingsdaten, aber nicht so toll für Testdaten"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "l3Nncu8nh5TC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "97b03bc6-0eab-4d8a-a16a-e48fd170d1cc"
      },
      "source": [
        "clf_sepal.score(X_train_sepal_only, y_train)"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9555555555555556"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "gfXXS3XXh5TE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "2e53750f-c8fd-4d21-bfef-4cfb3c19ac17"
      },
      "source": [
        "clf_sepal.score(X_test_sepal_only, y_test)"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mVMqrNiQh5TG",
        "colab_type": "text"
      },
      "source": [
        "### Das sieht nach Overfittung aus, das siehst du auch beim Plotting der Deciscion Boundaries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "KqGWSCNMh5TG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "b435261a-9e9d-4d82-b434-8799352562f7"
      },
      "source": [
        "plotPrediction(clf_sepal, X_train_sepal_only[:, 0], X_train_sepal_only[:, 1], \n",
        "               'Sepal length', 'Sepal width', y_train,\n",
        "               title=\"Highly Fragmented Decision Boundaries for Train Data\")\n",
        "# plt.savefig('ML_0202.png', bbox_inches='tight')"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "-Zdxi--uh5TI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "3c65bc3b-7f7b-48dc-ae4d-f5c6d170fac1"
      },
      "source": [
        "plotPrediction(clf_sepal, X_test_sepal_only[:, 0], X_test_sepal_only[:, 1],\n",
        "               'Sepal length', 'Sepal width', y_test,\n",
        "               title=\"Same Decision Boundaries don't work well for Test Data\")\n",
        "# plt.savefig('ML_0203.png', bbox_inches='tight')"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLSNHLkah5TK",
        "colab_type": "text"
      },
      "source": [
        "## Wir machen das Modell weniger komplex, allgemeiner"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "lgImHWr_h5TK",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "8094ca7b-38ee-4141-de08-d09bc80cf4fc"
      },
      "source": [
        "clf_sepal_10 = neighbors.KNeighborsClassifier(10)\n",
        "clf_sepal_10.fit(X_train_sepal_only, y_train)"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "suI_eT6kh5TM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "75d3202f-8f36-47d8-c708-e285e0db0691"
      },
      "source": [
        "clf_sepal_10.score(X_train_sepal_only, y_train)"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "SSaIROFjh5TN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "5727baa1-d5f3-4d74-804f-fb2f9f87ee01"
      },
      "source": [
        "clf_sepal_10.score(X_test_sepal_only, y_test)"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "gtARmyn5h5TP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "752a5d42-7ad1-4ca9-da79-54f8cd1a4129"
      },
      "source": [
        "plotPrediction(clf_sepal_10, X_train_sepal_only[:, 0], X_train_sepal_only[:, 1], \n",
        "               'Sepal length', 'Sepal width', y_train,\n",
        "               title=\"Model too simple even for Train Data\")\n",
        "# plt.savefig('ML_0204.png', bbox_inches='tight')"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pLT4sUoPh5TR",
        "colab_type": "text"
      },
      "source": [
        "## Mit den Sepal Features werden wir immer entweder overfitten oder underfitten\n",
        "## Wir versuchen es noch einmal mit den Petal Features"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "DsxUwXQlh5TS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train_petal_only = X_train[:, 2:]\n",
        "X_test_petal_only = X_test[:, 2:]"
      ],
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "rT1DmQhVh5TV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "5d2f6bf2-ae5f-4ca9-d978-7818d9eae011"
      },
      "source": [
        "clf_petal_10 = neighbors.KNeighborsClassifier(10)\n",
        "clf_petal_10.fit(X_train_petal_only, y_train)"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
              "                     metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
              "                     weights='uniform')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "8SNy_jXih5TX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "6123fbe1-ddf5-45d4-f7b5-9b5740cdb69d"
      },
      "source": [
        "clf_petal_10.score(X_train_petal_only, y_train)"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "RuFFGUOoh5Tb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e90b8b0-0fc3-4326-ce9f-6f7da72916f8"
      },
      "source": [
        "clf_petal_10.score(X_test_petal_only, y_test)"
      ],
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.95"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "0M_DYyT7h5Td",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "d816a589-0c2f-4a18-a556-49dd9be0edbc"
      },
      "source": [
        "plotPrediction(clf_petal_10, X_train_petal_only[:, 0], X_train_petal_only[:, 1], \n",
        "               'Petal length', 'Petal width', y_train,\n",
        "               title=\"Simple model looks good for Train Data\")\n",
        "# plt.savefig('ML_0205.png', bbox_inches='tight')"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "trusted": false,
        "id": "n5VEEL4xh5Th",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "outputId": "a549395d-f5c0-47b5-987e-4a2955b03e0a"
      },
      "source": [
        "plotPrediction(clf_petal_10, X_test_petal_only[:, 0], X_test_petal_only[:, 1], \n",
        "               'Petal length', 'Petal width', y_test,\n",
        "               title=\"Simple model looks good even for Test Data\")\n",
        "# plt.savefig('ML_0206.png', bbox_inches='tight')"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "b6EUhrckh5Tk",
        "colab_type": "text"
      },
      "source": [
        "Ein deutlich besseres Ergebnis, obwohl wir wieder nur 2 Features genommen haben. Es kann also entscheident sein, welche Features man nimmt."
      ]
    }
  ]
}