4-1.ipynb

{
  "cells": [
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      "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": "f99eefa2-a30d-4471-d3aa-586aa150e9a4"
      },
      "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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              "  <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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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-daf58e02-490a-407d-8b82-23fc51ecd5ea');\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() # 앞에 있는 다섯 개의 데이터 보여줌"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UWJWlRCHVWUg",
        "outputId": "08983d78-93a3-4581-dd7d-79cba672a97a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Roach' 'Whitefish' 'Parkki' 'Perch' 'Pike' 'Smelt']\n"
          ]
        }
      ],
      "source": [
        "print(pd.unique(fish['Species'])) # Species 열에 있는 모든 물고기 종류"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "8JjMXc9wVE7C"
      },
      "outputs": [],
      "source": [
        "fish_input = fish[['Weight','Length','Diagonal','Height','Width']].to_numpy() # Species 열을 제외한 모든 열을 numpy 배열로 바꿔 저장"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1T6C1d5iMzb8",
        "outputId": "a7b71f0a-beb2-42f1-c6ec-402bb6a5c749",
        "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() # Species 열은 타겟"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "dkllezAJW63K"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split # 훈련 세트와 테스트 세트 분리\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 # 표준화\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": "fca6418e-0014-4441-dd2d-8b2194b23ba1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.8907563025210085\n",
            "0.85\n"
          ]
        }
      ],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier # k-최근접 이웃 분류\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": "3c351241-a884-4944-ac20-c79c436a9f87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Parkki' 'Perch' 'Pike' 'Roach' 'Smelt' 'Whitefish']\n"
          ]
        }
      ],
      "source": [
        "print(kn.classes_) # 알파벳 순으로 정렬되어 사용되는 class들"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EucmtF8HVOS_",
        "outputId": "5ad14cf0-5028-4a07-d852-4ec9c14c06d7"
      },
      "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": "9008aa3b-77b0-4d02-9d83-41614e6c011f"
      },
      "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]) # predict_proba : 각 클래스들의 예측 확률\n",
        "print(np.round(proba, decimals=4)) # 소수 넷째 자리에서 반올림"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hk-ywsfKkf7t",
        "outputId": "97ac1a49-cc10-4526-8878-b142a2423901"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Roach' 'Perch' 'Perch']]\n"
          ]
        }
      ],
      "source": [
        "distances, indexes = kn.kneighbors(test_scaled[3:4]) # 2차원 배열을 가져오기 위해 슬라이싱을 사용했음\n",
        "print(train_target[indexes]) # 4번째 데이터의 이웃 확인"
      ]
    },
    {
      "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": "390bbe87-461a-4ac5-8400-50e57b4cbf9f"
      },
      "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)) # 시그모이드 함수(값을 0~1사이로 압축)\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": "bd82eb0d-7f82-4733-fd2d-66549fef926f"
      },
      "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]]) # 불리언 인덱싱"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Khxh-3t5-2Tk"
      },
      "outputs": [],
      "source": [
        "# 타겟 값이 도미이거나 빙어인 데이터만 저장\n",
        "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/"
        },
        "id": "jEzP0aeXANra",
        "outputId": "572a7813-1ca2-4969-c8c9-860f84cc6fd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "from sklearn.linear_model import LogisticRegression # 로지스틱 회귀(분류 문제에 사용)\n",
        "\n",
        "lr = LogisticRegression()\n",
        "lr.fit(train_bream_smelt, target_bream_smelt) # 이진 분류"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VtEWtsB7EIgm",
        "outputId": "edb7f9c7-4134-4a74-cc55-02f899513257"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt' 'Bream' 'Bream' 'Bream']\n"
          ]
        }
      ],
      "source": [
        "print(lr.predict(train_bream_smelt[:5])) # 앞 다섯 개의 값 예측"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3H_qieV-_CTt",
        "outputId": "4eee524a-c8e4-46d0-d027-36b2d266be99"
      },
      "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])) # 음성 클래스(0)의 확률, 양성 클래스(1)의 확률"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mm60bpr7EQKU",
        "outputId": "abb538af-a020-437d-bd58-30426b636e65"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Bream' 'Smelt']\n"
          ]
        }
      ],
      "source": [
        "print(lr.classes_) # Bream이 음성 클래스, Smelt가 양성 클래스"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1mvoYhUVQmFY",
        "outputId": "2ad01f8e-1fb3-46b5-e5ad-20688033c2a4"
      },
      "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_) # 계수 확인"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SxrRy9m8A5Hy",
        "outputId": "6f4ae25f-82c3-414f-a3b3-18e275ca85c5"
      },
      "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]) # decision_function : z값\n",
        "print(decisions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SeuhSRuiA9yZ",
        "outputId": "72305ffc-c47a-4f93-d304-71d4013924d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.00240145 0.97264817 0.00513928 0.01415798 0.00232731]\n"
          ]
        }
      ],
      "source": [
        "from scipy.special import expit # 시그모이드 함수\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": "4c8d7eba-8e1e-43b3-90bb-7c98c8331f6e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.9327731092436975\n",
            "0.925\n"
          ]
        }
      ],
      "source": [
        "lr = LogisticRegression(C=20, max_iter=1000) # max_iter : 반복 횟수\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": "e5b9f63c-0fea-4fa7-db4f-230acacde434"
      },
      "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": "e391ab27-25d5-4e10-e08e-23f5eda43c99"
      },
      "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]) # 다중 분류의 예측 확률은 softmax 함수로 계산됨\n",
        "print(np.round(proba, decimals=3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXASv4WU87UF",
        "outputId": "9105500b-8485-4e33-dae7-c9a880266726"
      },
      "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": "60296ffb-3f3e-4b89-e790-9050429ccda0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(7, 5) (7,)\n"
          ]
        }
      ],
      "source": [
        "print(lr.coef_.shape, lr.intercept_.shape) # 7개의 클래스, 5개의 특성"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s9iRz1iAd7Oe",
        "outputId": "4006f549-1bc8-42af-e0d7-0c9c890f042e"
      },
      "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]) # z값 확인\n",
        "print(np.round(decision, decimals=2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "49CcsDHZeJma",
        "outputId": "d5fe9d20-ccc7-401b-b8ed-21d8c930a2ec"
      },
      "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 # softmax 함수 적용\n",
        "\n",
        "proba = softmax(decision, axis=1)\n",
        "print(np.round(proba, decimals=3))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "NeCtfNshc5oj"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "4-1 로지스틱 회귀.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.7.3"
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  "nbformat": 4,
  "nbformat_minor": 0
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