4-1.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LlS4gyYqJVpn"
      },
      "source": [
        "# 로지스틱 회귀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m9MHDgx2JVpv"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/4-1.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQBDVUjBbIsL"
      },
      "source": [
        "## 럭키백의 확률"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ILi_LPl9JVpw"
      },
      "source": [
        "### 데이터 준비하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Mba6QeEmLn3r",
        "outputId": "57a3f7b0-a070-4150-8c0f-1685120b01bf"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Species  Weight  Length  Diagonal   Height   Width\n",
              "0   Bream   242.0    25.4      30.0  11.5200  4.0200\n",
              "1   Bream   290.0    26.3      31.2  12.4800  4.3056\n",
              "2   Bream   340.0    26.5      31.1  12.3778  4.6961\n",
              "3   Bream   363.0    29.0      33.5  12.7300  4.4555\n",
              "4   Bream   430.0    29.0      34.0  12.4440  5.1340"
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Species</th>\n",
              "      <th>Weight</th>\n",
              "      <th>Length</th>\n",
              "      <th>Diagonal</th>\n",
              "      <th>Height</th>\n",
              "      <th>Width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Bream</td>\n",
              "      <td>242.0</td>\n",
              "      <td>25.4</td>\n",
              "      <td>30.0</td>\n",
              "      <td>11.5200</td>\n",
              "      <td>4.0200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bream</td>\n",
              "      <td>290.0</td>\n",
              "      <td>26.3</td>\n",
              "      <td>31.2</td>\n",
              "      <td>12.4800</td>\n",
              "      <td>4.3056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Bream</td>\n",
              "      <td>340.0</td>\n",
              "      <td>26.5</td>\n",
              "      <td>31.1</td>\n",
              "      <td>12.3778</td>\n",
              "      <td>4.6961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bream</td>\n",
              "      <td>363.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>33.5</td>\n",
              "      <td>12.7300</td>\n",
              "      <td>4.4555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bream</td>\n",
              "      <td>430.0</td>\n",
              "      <td>29.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>12.4440</td>\n",
              "      <td>5.1340</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "          document.querySelector('#df-318f7db9-2b07-41e2-be5e-890eb1202985 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-318f7db9-2b07-41e2-be5e-890eb1202985');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "import pandas as pd\n",
        "\n",
        "fish = pd.read_csv('https://bit.ly/fish_csv_data')\n",
        "fish.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWJWlRCHVWUg",
        "outputId": "e8677ae1-93ee-42b3-b006-8aebdd52ddea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n"
          ]
        }
      ],
      "source": [
        "print(pd.unique(fish['Species']))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "8JjMXc9wVE7C"
      },
      "outputs": [],
      "source": [
        "fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1T6C1d5iMzb8",
        "outputId": "d478b47a-5548-4b9c-b161-a6bd042809ce",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[242.      25.4     30.      11.52     4.02  ]\n",
            " [290.      26.3     31.2     12.48     4.3056]\n",
            " [340.      26.5     31.1     12.3778   4.6961]\n",
            " [363.      29.      33.5     12.73     4.4555]\n",
            " [430.      29.      34.      12.444    5.134 ]]\n"
          ]
        }
      ],
      "source": [
        "print(fish_input[:5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "aB2oHhojTfWE"
      },
      "outputs": [],
      "source": [
        "fish_target = fish['Species'].to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dkllezAJW63K"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, test_input, train_target, test_target = train_test_split(\n",
        "    fish_input, fish_target, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "H0ujq0BjXpfp"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "ss = StandardScaler()\n",
        "ss.fit(train_input)\n",
        "train_scaled = ss.transform(train_input)\n",
        "test_scaled = ss.transform(test_input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oAxk-V5kQcgc"
      },
      "source": [
        "### k-최근접 이웃 분류기의 확률 예측"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BYWTNPOdXOfr",
        "outputId": "73eaa7ab-2c06-47d6-976f-b581c6abd781"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8907563025210085\n",
            "0.85\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "\n",
        "kn = KNeighborsClassifier(n_neighbors=3)\n",
        "kn.fit(train_scaled, train_target)\n",
        "\n",
        "print(kn.score(train_scaled, train_target))\n",
        "print(kn.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a879-O42RhFO",
        "outputId": "fedf851e-9e20-46dc-c744-bd0a6f5f3888"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
          ]
        }
      ],
      "source": [
        "print(kn.classes_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EucmtF8HVOS_",
        "outputId": "3a7e4d52-e08e-4f62-d02e-fc2e261f7df4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Perch' 'Smelt' 'Pike' 'Perch' 'Perch']\n"
          ]
        }
      ],
      "source": [
        "print(kn.predict(test_scaled[:5]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSDr8WSKXbUa",
        "outputId": "65eb994a-6619-440c-f300-de1f17e22640"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.     0.     1.     0.     0.     0.     0.    ]\n",
            " [0.     0.     0.     0.     0.     1.     0.    ]\n",
            " [0.     0.     0.     1.     0.     0.     0.    ]\n",
            " [0.     0.     0.6667 0.     0.3333 0.     0.    ]\n",
            " [0.     0.     0.6667 0.     0.3333 0.     0.    ]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "proba = kn.predict_proba(test_scaled[:5])\n",
        "print(np.round(proba, decimals=4))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hk-ywsfKkf7t",
        "outputId": "f4a95e94-5a1d-456e-f731-337e482b2015"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Roach' 'Perch' 'Perch']]\n"
          ]
        }
      ],
      "source": [
        "distances, indexes = kn.kneighbors(test_scaled[3:4])\n",
        "print(train_target[indexes])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q9_wuI_0tEqL"
      },
      "source": [
        "## 로지스틱 회귀"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "8rdDSaZ5uji2",
        "outputId": "84b2ba1e-0b49-4c16-920d-a59bbe94b1e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "z = np.arange(-5, 5, 0.1)\n",
        "phi = 1 / (1 + np.exp(-z))\n",
        "\n",
        "plt.plot(z, phi)\n",
        "plt.xlabel('z')\n",
        "plt.ylabel('phi')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1J6LGKpUJbFE"
      },
      "source": [
        "### 로지스틱 회귀로 이진 분류 수행하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JeR5cA_fIe24",
        "outputId": "5c61985e-3ce1-4608-f50b-5d3e47a453d2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['A' 'C']\n"
          ]
        }
      ],
      "source": [
        "char_arr = np.array(['A', 'B', 'C', 'D', 'E'])\n",
        "print(char_arr[[True, False, True, False, False]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Khxh-3t5-2Tk"
      },
      "outputs": [],
      "source": [
        "bream_smelt_indexes = (train_target == 'Bream') | (train_target == 'Smelt')\n",
        "train_bream_smelt = train_scaled[bream_smelt_indexes]\n",
        "target_bream_smelt = train_target[bream_smelt_indexes]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "jEzP0aeXANra",
        "outputId": "97ec15df-3cd7-4053-c874-127a7f6f83e7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "lr = LogisticRegression()\n",
        "lr.fit(train_bream_smelt, target_bream_smelt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtEWtsB7EIgm",
        "outputId": "4dc0481d-5f50-4fdd-ad1f-95e3fba5d52c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt' 'Bream' 'Bream' 'Bream']\n"
          ]
        }
      ],
      "source": [
        "print(lr.predict(train_bream_smelt[:5]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H_qieV-_CTt",
        "outputId": "2009c84a-d690-4a8d-94b2-2bcba5e022b0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.99759855 0.00240145]\n",
            " [0.02735183 0.97264817]\n",
            " [0.99486072 0.00513928]\n",
            " [0.98584202 0.01415798]\n",
            " [0.99767269 0.00232731]]\n"
          ]
        }
      ],
      "source": [
        "print(lr.predict_proba(train_bream_smelt[:5]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mm60bpr7EQKU",
        "outputId": "4543eba6-cf01-457a-ba6e-fe51ff639c2b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt']\n"
          ]
        }
      ],
      "source": [
        "print(lr.classes_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mvoYhUVQmFY",
        "outputId": "993fae15-10c7-4143-fe44-c7ee74ea5f3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-0.4037798  -0.57620209 -0.66280298 -1.01290277 -0.73168947]] [-2.16155132]\n"
          ]
        }
      ],
      "source": [
        "print(lr.coef_, lr.intercept_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxrRy9m8A5Hy",
        "outputId": "3ae32b5f-4389-4b52-f53d-2164d6cf2827"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-6.02927744  3.57123907 -5.26568906 -4.24321775 -6.0607117 ]\n"
          ]
        }
      ],
      "source": [
        "decisions = lr.decision_function(train_bream_smelt[:5])\n",
        "print(decisions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeuhSRuiA9yZ",
        "outputId": "75e2b0e0-3309-4322-e70f-fa436fe87049"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00240145 0.97264817 0.00513928 0.01415798 0.00232731]\n"
          ]
        }
      ],
      "source": [
        "from scipy.special import expit\n",
        "\n",
        "print(expit(decisions))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ee-s4l7EuVo"
      },
      "source": [
        "### 로지스틱 회귀로 다중 분류 수행하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7QugsbD2X8bf",
        "outputId": "f8b8c98e-591d-4953-bcd3-14cf4ee88024"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9327731092436975\n",
            "0.925\n"
          ]
        }
      ],
      "source": [
        "lr = LogisticRegression(C=20, max_iter=1000)\n",
        "lr.fit(train_scaled, train_target)\n",
        "\n",
        "print(lr.score(train_scaled, train_target))\n",
        "print(lr.score(test_scaled, test_target))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0taO0XnF9dha",
        "outputId": "446681f8-6dc2-4ac4-b329-e3702feda6cc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Perch' 'Smelt' 'Pike' 'Roach' 'Perch']\n"
          ]
        }
      ],
      "source": [
        "print(lr.predict(test_scaled[:5]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqZosYezZOi3",
        "outputId": "2dc8fa5e-ee86-434f-93ef-e5ff38135fb4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
          ]
        }
      ],
      "source": [
        "proba = lr.predict_proba(test_scaled[:5])\n",
        "print(np.round(proba, decimals=3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXASv4WU87UF",
        "outputId": "ec2f6677-21b6-4314-c475-ca319b818320"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
          ]
        }
      ],
      "source": [
        "print(lr.classes_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1swPv6ZOZTjg",
        "outputId": "df10e772-4928-45b2-c616-4994467aeac9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7, 5) (7,)\n"
          ]
        }
      ],
      "source": [
        "print(lr.coef_.shape, lr.intercept_.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9iRz1iAd7Oe",
        "outputId": "a00a1d10-deca-4057-b1d1-cd9fe93b48b3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ -6.5    1.03   5.16  -2.73   3.34   0.33  -0.63]\n",
            " [-10.86   1.93   4.77  -2.4    2.98   7.84  -4.26]\n",
            " [ -4.34  -6.23   3.17   6.49   2.36   2.42  -3.87]\n",
            " [ -0.68   0.45   2.65  -1.19   3.26  -5.75   1.26]\n",
            " [ -6.4   -1.99   5.82  -0.11   3.5   -0.11  -0.71]]\n"
          ]
        }
      ],
      "source": [
        "decision = lr.decision_function(test_scaled[:5])\n",
        "print(np.round(decision, decimals=2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49CcsDHZeJma",
        "outputId": "04389c27-a5fb-4273-840b-3365e3d98235"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.    0.014 0.841 0.    0.136 0.007 0.003]\n",
            " [0.    0.003 0.044 0.    0.007 0.946 0.   ]\n",
            " [0.    0.    0.034 0.935 0.015 0.016 0.   ]\n",
            " [0.011 0.034 0.306 0.007 0.567 0.    0.076]\n",
            " [0.    0.    0.904 0.002 0.089 0.002 0.001]]\n"
          ]
        }
      ],
      "source": [
        "from scipy.special import softmax\n",
        "\n",
        "proba = softmax(decision, axis=1)\n",
        "print(np.round(proba, decimals=3))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "4-1 로지스틱 회귀.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}