UCI糖尿病数据利用逻辑回归算法进行训练和预测_uci数据集逻辑回归 python 代码

作者：从前慢现在也慢 | 2024-05-30 00:21:18
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uci数据集逻辑回归 python 代码
UCI糖尿病数据利用逻辑回归算法进行训练和预测
jupyter
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "diabetes_data = pd.read_csv('diabetes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0            6      148             72             35        0  33.6   \n",
       "1            1       85             66             29        0  26.6   \n",
       "2            8      183             64              0        0  23.3   \n",
       "3            1       89             66             23       94  28.1   \n",
       "4            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigmoid(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dd0d80a908>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xx = np.arange(-10,10,step=1)\n",
    "yy = sigmoid(xx)\n",
    "plt.grid()\n",
    "plt.plot(xx, yy, 'r')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logistic_model(X, theta):\n",
    "    temp = np.dot(X,theta.T)\n",
    "    return sigmoid(temp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "diabetes_data.insert(0,'Ones',1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ones</th>\n",
       "      <th>Pregnancies</th>\n",
       "      <th>Glucose</th>\n",
       "      <th>BloodPressure</th>\n",
       "      <th>SkinThickness</th>\n",
       "      <th>Insulin</th>\n",
       "      <th>BMI</th>\n",
       "      <th>DiabetesPedigreeFunction</th>\n",
       "      <th>Age</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Ones  Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
       "0     1            6      148             72             35        0  33.6   \n",
       "1     1            1       85             66             29        0  26.6   \n",
       "2     1            8      183             64              0        0  23.3   \n",
       "3     1            1       89             66             23       94  28.1   \n",
       "4     1            0      137             40             35      168  43.1   \n",
       "\n",
       "   DiabetesPedigreeFunction  Age  Outcome  \n",
       "0                     0.627   50        1  \n",
       "1                     0.351   31        0  \n",
       "2                     0.672   32        1  \n",
       "3                     0.167   21        0  \n",
       "4                     2.288   33        1  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diabetes_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "orig_data = diabetes_data.values\n",
    "cols = orig_data.shape[1]\n",
    "X = orig_data[:,0:cols-1]    #X大写，表示是一个矩阵\n",
    "y = orig_data[:,cols-1:cols] #y小写，表示矢量\n",
    "theta = np.zeros([1,cols-1]) #theta设为行向量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cost(X, y, theta):\n",
    "    item1 = np.multiply(y, np.log(logistic_model(X,theta))) \n",
    "    item2 = np.multiply(1-y, np.log(1 - logistic_model(X,theta))) \n",
    "    return np.sum(item1 - item2) / (len(X))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20938821079415015"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cost(X, y, theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gradient(X, y, theta):\n",
    "    grad = np.zeros(theta.shape) #一个梯度是对theta求导的\n",
    "    error = (logistic_model(X,theta) - y).ravel()\n",
    "    for j in range(len(theta.ravel())):\n",
    "        temp = np.multiply(error, X[:,j])\n",
    "        grad[0,j] = np.sum(temp) / len(X)\n",
    "    return grad"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#拟合 fit,求模型的参数，也称为训练的过程，\n",
    "def fit(X, y, theta, iter_num = 5000, alpha=0.00001):\n",
    "    #梯度下降求解\n",
    "    i = 0 # 迭代次数\n",
    "    grad = np.zeros(theta.shape) # 计算的梯度\n",
    "    costs = [cost(X, y, theta)] # 损失值\n",
    "    while True:\n",
    "        grad = gradient(X, y, theta)\n",
    "        theta = theta - alpha*grad # 参数更新\n",
    "        costs.append(cost(X, y, theta)) # 计算新的损失\n",
    "        i += 1 \n",
    "        if i % (iter_num / 10) == 0: print(costs[i])\n",
    "        if i > iter_num: break\n",
    "    \n",
    "    return theta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in multiply\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-5.511409235893268\n",
      "-5.31859458354884\n",
      "-5.063153190867181\n",
      "-4.696751678117883\n",
      "-4.074011252701483\n",
      "-3.902875016698084\n",
      "-3.744762960211906\n",
      "-3.601077279719243\n",
      "-3.4711575061505564\n",
      "-3.352062637279824\n"
     ]
    }
   ],
   "source": [
    "# 调参，只能根据经验来，炼丹\n",
    "theta = fit(X, y, theta,iter_num = 500000, alpha=0.0015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\model_selection\\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size = .8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(614, 9)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta = np.zeros([1,cols-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n",
      "D:\\Anaconda\\lib\\site-packages\\ipykernel_launcher.py:3: RuntimeWarning: invalid value encountered in multiply\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-3.4953304853973584\n",
      "-4.075793679628199\n",
      "-3.7134641993740907\n",
      "-3.402073232763518\n",
      "-3.155158733286961\n",
      "-2.9550791865014996\n",
      "-2.785268118118423\n",
      "-2.6323045981679933\n",
      "-2.4989764065989086\n",
      "-2.3919272008733246\n"
     ]
    }
   ],
   "source": [
    "theta = fit(X_train, y_train, theta,iter_num = 1000000, alpha=0.0015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 测试过程\n",
    "def predict(X,theta, threshold = 0.5):\n",
    "    p = logistic_model(X, theta)\n",
    "    #分类，如果这个概率大于0.5，分类为1，否则为0\n",
    "    y = np.where(p > threshold, 1, 0)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# shift + enter\n",
    "myPredict_y = predict(X_test, theta, threshold = 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>20</th>\n",
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       "      <td>1.0</td>\n",
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       "      <th>124</th>\n",
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       "      <th>126</th>\n",
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       "      <th>127</th>\n",
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       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>137</th>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>139</th>\n",
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       "      <th>140</th>\n",
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       "      <th>146</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>154 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       0  MyPredict\n",
       "0    0.0          0\n",
       "1    1.0          1\n",
       "2    1.0          1\n",
       "3    0.0          0\n",
       "4    0.0          0\n",
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       "6    1.0          1\n",
       "7    1.0          1\n",
       "8    0.0          0\n",
       "9    1.0          1\n",
       "10   0.0          0\n",
       "11   0.0          1\n",
       "12   0.0          0\n",
       "13   1.0          1\n",
       "14   0.0          1\n",
       "15   0.0          1\n",
       "16   0.0          0\n",
       "17   0.0          0\n",
       "18   0.0          1\n",
       "19   1.0          1\n",
       "20   0.0          1\n",
       "21   1.0          1\n",
       "22   0.0          0\n",
       "23   0.0          1\n",
       "24   0.0          1\n",
       "25   0.0          0\n",
       "26   0.0          0\n",
       "27   1.0          1\n",
       "28   1.0          1\n",
       "29   0.0          0\n",
       "..   ...        ...\n",
       "124  1.0          1\n",
       "125  1.0          1\n",
       "126  1.0          1\n",
       "127  1.0          1\n",
       "128  0.0          1\n",
       "129  1.0          0\n",
       "130  0.0          1\n",
       "131  0.0          0\n",
       "132  1.0          1\n",
       "133  0.0          1\n",
       "134  0.0          1\n",
       "135  1.0          1\n",
       "136  1.0          1\n",
       "137  1.0          1\n",
       "138  0.0          0\n",
       "139  0.0          1\n",
       "140  0.0          0\n",
       "141  0.0          0\n",
       "142  0.0          1\n",
       "143  0.0          1\n",
       "144  0.0          0\n",
       "145  0.0          0\n",
       "146  0.0          0\n",
       "147  0.0          0\n",
       "148  0.0          0\n",
       "149  0.0          0\n",
       "150  0.0          1\n",
       "151  1.0          1\n",
       "152  0.0          1\n",
       "153  0.0          1\n",
       "\n",
       "[154 rows x 2 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 要看预测的值和真实值y_test之间的差别\n",
    "yy = pd.DataFrame(y_test)\n",
    "yy[\"MyPredict\"] = myPredict_y\n",
    "yy\n",
    "#20个数据对了16个， 80%准确率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\utils\\validation.py:578: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看sklearn的函数的表现\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 1., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0.,\n",
       "       0., 1., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 1.,\n",
       "       0., 0., 1., 0., 0., 0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 1., 0., 1., 0., 0., 0., 1., 0., 0., 0., 1., 0., 1., 0., 0., 0.,\n",
       "       0., 0., 0., 0., 0., 0., 1., 0., 1., 0., 0., 0., 0., 0., 1., 0., 0.,\n",
       "       0., 1., 0., 1., 1., 0., 0., 1., 0., 0., 0., 0., 0., 1., 1., 0., 1.,\n",
       "       0., 1., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
       "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 1.,\n",
       "       0., 0., 0., 1., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 1., 1., 0.,\n",
       "       1.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1</td>\n",
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       "      <th>19</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>21</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>22</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>137</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>0.0</td>\n",
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       "      <th>139</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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       "      <th>140</th>\n",
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       "      <th>141</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "       0  MyPredict\n",
       "0    0.0          0\n",
       "1    1.0          1\n",
       "2    1.0          1\n",
       "3    0.0          0\n",
       "4    0.0          0\n",
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       "11   0.0          1\n",
       "12   0.0          0\n",
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       "26   0.0          0\n",
       "27   1.0          1\n",
       "28   1.0          1\n",
       "29   0.0          0\n",
       "..   ...        ...\n",
       "124  1.0          1\n",
       "125  1.0          1\n",
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       "128  0.0          1\n",
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       "137  1.0          1\n",
       "138  0.0          0\n",
       "139  0.0          1\n",
       "140  0.0          0\n",
       "141  0.0          0\n",
       "142  0.0          1\n",
       "143  0.0          1\n",
       "144  0.0          0\n",
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       "150  0.0          1\n",
       "151  1.0          1\n",
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       "153  0.0          1\n",
       "\n",
       "[154 rows x 2 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yy"
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UCI糖尿病数据利用逻辑回归算法进行训练和预测_uci数据集 逻辑回归 python 代码

UCI糖尿病数据利用逻辑回归算法进行训练和预测_uci数据集逻辑回归 python 代码
