Finance.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#判定贷款用户是否逾期\" data-toc-modified-id=\"判定贷款用户是否逾期-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>判定贷款用户是否逾期</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据集预览\" data-toc-modified-id=\"数据集预览-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>数据集预览</a></span></li><li><span><a href=\"#数据预处理\" data-toc-modified-id=\"数据预处理-1.2\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>数据预处理</a></span><ul class=\"toc-item\"><li><span><a href=\"#删除无用特征\" data-toc-modified-id=\"删除无用特征-1.2.1\"><span class=\"toc-item-num\">1.2.1&nbsp;&nbsp;</span>删除无用特征</a></span></li><li><span><a href=\"#字符型特征-编码\" data-toc-modified-id=\"字符型特征-编码-1.2.2\"><span class=\"toc-item-num\">1.2.2&nbsp;&nbsp;</span>字符型特征-编码</a></span></li><li><span><a href=\"#缺失值处理\" data-toc-modified-id=\"缺失值处理-1.2.3\"><span class=\"toc-item-num\">1.2.3&nbsp;&nbsp;</span>缺失值处理</a></span></li></ul></li><li><span><a href=\"#特征工程\" data-toc-modified-id=\"特征工程-1.3\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>特征工程</a></span></li><li><span><a href=\"#模型选择\" data-toc-modified-id=\"模型选择-1.4\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>模型选择</a></span><ul class=\"toc-item\"><li><span><a href=\"#数据集划分\" data-toc-modified-id=\"数据集划分-1.4.1\"><span class=\"toc-item-num\">1.4.1&nbsp;&nbsp;</span>数据集划分</a></span></li><li><span><a href=\"#LR模型\" data-toc-modified-id=\"LR模型-1.4.2\"><span class=\"toc-item-num\">1.4.2&nbsp;&nbsp;</span>LR模型</a></span></li><li><span><a href=\"#SVM模型\" data-toc-modified-id=\"SVM模型-1.4.3\"><span class=\"toc-item-num\">1.4.3&nbsp;&nbsp;</span>SVM模型</a></span></li><li><span><a href=\"#决策树模型\" data-toc-modified-id=\"决策树模型-1.4.4\"><span class=\"toc-item-num\">1.4.4&nbsp;&nbsp;</span>决策树模型</a></span></li><li><span><a href=\"#XGBoost模型\" data-toc-modified-id=\"XGBoost模型-1.4.5\"><span class=\"toc-item-num\">1.4.5&nbsp;&nbsp;</span>XGBoost模型</a></span></li><li><span><a href=\"#LightGBM模型\" data-toc-modified-id=\"LightGBM模型-1.4.6\"><span class=\"toc-item-num\">1.4.6&nbsp;&nbsp;</span>LightGBM模型</a></span></li></ul></li><li><span><a href=\"#模型调参\" data-toc-modified-id=\"模型调参-1.5\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>模型调参</a></span></li><li><span><a href=\"#模型评估\" data-toc-modified-id=\"模型评估-1.6\"><span class=\"toc-item-num\">1.6&nbsp;&nbsp;</span>模型评估</a></span></li></ul></li><li><span><a href=\"#遇到的问题\" data-toc-modified-id=\"遇到的问题-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>遇到的问题</a></span></li><li><span><a href=\"#Reference\" data-toc-modified-id=\"Reference-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Reference</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 判定贷款用户是否逾期"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给定金融数据，预测贷款用户是否会逾期。\n",
    "（status是标签：0表示未逾期，1表示逾期。）\n",
    "\n",
    "**Task1** - 构建逻辑回归模型进行预测（在构建部分数据需要进行缺失值处理和数据类型转换，如果不能处理，可以直接暴力删除）\n",
    "\n",
    "**Task2** - 构建SVM和决策树模型进行预测\n",
    "\n",
    "**Task3** - 构建xgboost和lightgbm模型进行预测\n",
    "\n",
    "**Task4（模型评估）** - 记录五个模型关于accuracy、precision和recall、f1-score、auc的评分表格，画出roc曲线\n",
    "\n",
    "**Task5（数据探索）** - 数据类型转换以及缺失值处理（尝试不同的填充看效果）以及你能借鉴的数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4754, 90)\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>custid</th>\n",
       "      <th>trade_no</th>\n",
       "      <th>bank_card_no</th>\n",
       "      <th>low_volume_percent</th>\n",
       "      <th>middle_volume_percent</th>\n",
       "      <th>take_amount_in_later_12_month_highest</th>\n",
       "      <th>trans_amount_increase_rate_lately</th>\n",
       "      <th>trans_activity_month</th>\n",
       "      <th>trans_activity_day</th>\n",
       "      <th>...</th>\n",
       "      <th>loans_max_limit</th>\n",
       "      <th>loans_avg_limit</th>\n",
       "      <th>consfin_credit_limit</th>\n",
       "      <th>consfin_credibility</th>\n",
       "      <th>consfin_org_count_current</th>\n",
       "      <th>consfin_product_count</th>\n",
       "      <th>consfin_max_limit</th>\n",
       "      <th>consfin_avg_limit</th>\n",
       "      <th>latest_query_day</th>\n",
       "      <th>loans_latest_day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>2791858</td>\n",
       "      <td>20180507115231274000000023057383</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.313</td>\n",
       "      <td>...</td>\n",
       "      <td>2900.0</td>\n",
       "      <td>1688.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>18.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>534047</td>\n",
       "      <td>20180507121002192000000023073000</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.94</td>\n",
       "      <td>2000</td>\n",
       "      <td>1.28</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.458</td>\n",
       "      <td>...</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>1758.0</td>\n",
       "      <td>15100.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22800.0</td>\n",
       "      <td>9360.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2849787</td>\n",
       "      <td>20180507125159718000000023114911</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.114</td>\n",
       "      <td>...</td>\n",
       "      <td>1600.0</td>\n",
       "      <td>1250.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>4200.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>13</td>\n",
       "      <td>1809708</td>\n",
       "      <td>20180507121358683000000388283484</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.57</td>\n",
       "      <td>0.777</td>\n",
       "      <td>...</td>\n",
       "      <td>3200.0</td>\n",
       "      <td>1541.0</td>\n",
       "      <td>16300.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>12180.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14</td>\n",
       "      <td>2499829</td>\n",
       "      <td>20180507115448545000000388205844</td>\n",
       "      <td>卡号1</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0</td>\n",
       "      <td>0.46</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.175</td>\n",
       "      <td>...</td>\n",
       "      <td>2300.0</td>\n",
       "      <td>1630.0</td>\n",
       "      <td>8300.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8400.0</td>\n",
       "      <td>8250.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>120.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 90 columns</p>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0   custid                          trade_no bank_card_no  \\\n",
       "0           5  2791858  20180507115231274000000023057383          卡号1   \n",
       "1          10   534047  20180507121002192000000023073000          卡号1   \n",
       "2          12  2849787  20180507125159718000000023114911          卡号1   \n",
       "3          13  1809708  20180507121358683000000388283484          卡号1   \n",
       "4          14  2499829  20180507115448545000000388205844          卡号1   \n",
       "\n",
       "   low_volume_percent  middle_volume_percent  \\\n",
       "0                0.01                   0.99   \n",
       "1                0.02                   0.94   \n",
       "2                0.04                   0.96   \n",
       "3                0.00                   0.96   \n",
       "4                0.01                   0.99   \n",
       "\n",
       "   take_amount_in_later_12_month_highest  trans_amount_increase_rate_lately  \\\n",
       "0                                      0                               0.90   \n",
       "1                                   2000                               1.28   \n",
       "2                                      0                               1.00   \n",
       "3                                   2000                               0.13   \n",
       "4                                      0                               0.46   \n",
       "\n",
       "   trans_activity_month  trans_activity_day        ...         \\\n",
       "0                  0.55               0.313        ...          \n",
       "1                  1.00               0.458        ...          \n",
       "2                  1.00               0.114        ...          \n",
       "3                  0.57               0.777        ...          \n",
       "4                  1.00               0.175        ...          \n",
       "\n",
       "   loans_max_limit  loans_avg_limit  consfin_credit_limit  \\\n",
       "0           2900.0           1688.0                1200.0   \n",
       "1           3500.0           1758.0               15100.0   \n",
       "2           1600.0           1250.0                4200.0   \n",
       "3           3200.0           1541.0               16300.0   \n",
       "4           2300.0           1630.0                8300.0   \n",
       "\n",
       "   consfin_credibility  consfin_org_count_current  consfin_product_count  \\\n",
       "0                 75.0                        1.0                    2.0   \n",
       "1                 80.0                        5.0                    6.0   \n",
       "2                 87.0                        1.0                    1.0   \n",
       "3                 80.0                        5.0                    5.0   \n",
       "4                 79.0                        2.0                    2.0   \n",
       "\n",
       "   consfin_max_limit  consfin_avg_limit  latest_query_day  loans_latest_day  \n",
       "0             1200.0             1200.0              12.0              18.0  \n",
       "1            22800.0             9360.0               4.0               2.0  \n",
       "2             4200.0             4200.0               2.0               6.0  \n",
       "3            30000.0            12180.0               2.0               4.0  \n",
       "4             8400.0             8250.0              22.0             120.0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv('data.csv')\n",
    "print(data.shape)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'custid', 'trade_no', 'bank_card_no',\n",
       "       'low_volume_percent', 'middle_volume_percent',\n",
       "       'take_amount_in_later_12_month_highest',\n",
       "       'trans_amount_increase_rate_lately', 'trans_activity_month',\n",
       "       'trans_activity_day', 'transd_mcc', 'trans_days_interval_filter',\n",
       "       'trans_days_interval', 'regional_mobility', 'student_feature',\n",
       "       'repayment_capability', 'is_high_user', 'number_of_trans_from_2011',\n",
       "       'first_transaction_time', 'historical_trans_amount',\n",
       "       'historical_trans_day', 'rank_trad_1_month', 'trans_amount_3_month',\n",
       "       'avg_consume_less_12_valid_month', 'abs',\n",
       "       'top_trans_count_last_1_month', 'avg_price_last_12_month',\n",
       "       'avg_price_top_last_12_valid_month', 'reg_preference_for_trad',\n",
       "       'trans_top_time_last_1_month', 'trans_top_time_last_6_month',\n",
       "       'consume_top_time_last_1_month', 'consume_top_time_last_6_month',\n",
       "       'cross_consume_count_last_1_month',\n",
       "       'trans_fail_top_count_enum_last_1_month',\n",
       "       'trans_fail_top_count_enum_last_6_month',\n",
       "       'trans_fail_top_count_enum_last_12_month',\n",
       "       'consume_mini_time_last_1_month',\n",
       "       'max_cumulative_consume_later_1_month',\n",
       "       'max_consume_count_later_6_month',\n",
       "       'railway_consume_count_last_12_month',\n",
       "       'pawns_auctions_trusts_consume_last_1_month',\n",
       "       'pawns_auctions_trusts_consume_last_6_month',\n",
       "       'jewelry_consume_count_last_6_month', 'status', 'source',\n",
       "       'first_transaction_day', 'trans_day_last_12_month', 'id_name',\n",
       "       'apply_score', 'apply_credibility', 'query_org_count',\n",
       "       'query_finance_count', 'query_cash_count', 'query_sum_count',\n",
       "       'latest_query_time', 'latest_one_month_apply',\n",
       "       'latest_three_month_apply', 'latest_six_month_apply', 'loans_score',\n",
       "       'loans_credibility_behavior', 'loans_count', 'loans_settle_count',\n",
       "       'loans_overdue_count', 'loans_org_count_behavior',\n",
       "       'consfin_org_count_behavior', 'loans_cash_count',\n",
       "       'latest_one_month_loan', 'latest_three_month_loan',\n",
       "       'latest_six_month_loan', 'history_suc_fee', 'history_fail_fee',\n",
       "       'latest_one_month_suc', 'latest_one_month_fail', 'loans_long_time',\n",
       "       'loans_latest_time', 'loans_credit_limit', 'loans_credibility_limit',\n",
       "       'loans_org_count_current', 'loans_product_count', 'loans_max_limit',\n",
       "       'loans_avg_limit', 'consfin_credit_limit', 'consfin_credibility',\n",
       "       'consfin_org_count_current', 'consfin_product_count',\n",
       "       'consfin_max_limit', 'consfin_avg_limit', 'latest_query_day',\n",
       "       'loans_latest_day'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察各列的属性名称\n",
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据预处理\n",
    "\n",
    "> 无用特征删除、字符型特征编码和缺失值填充。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  删除无用特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'bank_card_no','source'的取值**无区分度**\n",
    "\n",
    "'Unnamed: 0', 'custid', 'trade_no'和id_name'**与预测无关**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4754, 84)\n"
     ]
    }
   ],
   "source": [
    "data.drop(['Unnamed: 0', 'custid', 'trade_no', 'bank_card_no', 'source', 'id_name'], \n",
    "          axis=1, inplace=True)\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**日期特征**（暂时删除, 以后再处理。）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data.drop(['first_transaction_time', 'latest_query_time', 'loans_latest_time'], \n",
    "          axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 字符型特征-编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "一线城市    3403\n",
       "三线城市    1064\n",
       "境外       150\n",
       "二线城市     131\n",
       "其他城市       4\n",
       "Name: reg_preference_for_trad, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['reg_preference_for_trad'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dic = {}\n",
    "for i, val in enumerate(list(data['reg_preference_for_trad'].unique())):\n",
    "    dic[val] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['reg_preference_for_trad'] = data['reg_preference_for_trad'].map(dic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 缺失值处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0421 % -- low_volume_percent\n",
      "0.0421 % -- middle_volume_percent\n",
      "0.0631 % -- trans_amount_increase_rate_lately\n",
      "0.0421 % -- trans_activity_month\n",
      "0.0421 % -- trans_activity_day\n",
      "0.0421 % -- transd_mcc\n",
      "0.1683 % -- trans_days_interval_filter\n",
      "0.0421 % -- trans_days_interval\n",
      "0.0421 % -- regional_mobility\n",
      "63.0627 % -- student_feature\n",
      "0.0421 % -- number_of_trans_from_2011\n",
      "0.0421 % -- historical_trans_day\n",
      "0.0421 % -- rank_trad_1_month\n",
      "0.0421 % -- avg_consume_less_12_valid_month\n",
      "0.0421 % -- top_trans_count_last_1_month\n",
      "2.1876 % -- avg_price_top_last_12_valid_month\n",
      "0.1683 % -- trans_top_time_last_1_month\n",
      "0.1683 % -- trans_top_time_last_6_month\n",
      "0.1683 % -- consume_top_time_last_1_month\n",
      "0.1683 % -- consume_top_time_last_6_month\n",
      "8.9609 % -- cross_consume_count_last_1_month\n",
      "0.3366 % -- trans_fail_top_count_enum_last_1_month\n",
      "0.3366 % -- trans_fail_top_count_enum_last_6_month\n",
      "0.3366 % -- trans_fail_top_count_enum_last_12_month\n",
      "0.5469 % -- consume_mini_time_last_1_month\n",
      "0.1683 % -- max_consume_count_later_6_month\n",
      "0.2524 % -- railway_consume_count_last_12_month\n",
      "0.2524 % -- jewelry_consume_count_last_6_month\n",
      "0.0421 % -- first_transaction_day\n",
      "0.0421 % -- trans_day_last_12_month\n",
      "6.3946 % -- apply_score\n",
      "6.3946 % -- apply_credibility\n",
      "6.3946 % -- query_org_count\n",
      "6.3946 % -- query_finance_count\n",
      "6.3946 % -- query_cash_count\n",
      "6.3946 % -- query_sum_count\n",
      "6.3946 % -- latest_one_month_apply\n",
      "6.3946 % -- latest_three_month_apply\n",
      "6.3946 % -- latest_six_month_apply\n",
      "6.2474 % -- loans_score\n",
      "6.2474 % -- loans_credibility_behavior\n",
      "6.2474 % -- loans_count\n",
      "6.2474 % -- loans_settle_count\n",
      "6.2474 % -- loans_overdue_count\n",
      "6.2474 % -- loans_org_count_behavior\n",
      "6.2474 % -- consfin_org_count_behavior\n",
      "6.2474 % -- loans_cash_count\n",
      "6.2474 % -- latest_one_month_loan\n",
      "6.2474 % -- latest_three_month_loan\n",
      "6.2474 % -- latest_six_month_loan\n",
      "6.2474 % -- history_suc_fee\n",
      "6.2474 % -- history_fail_fee\n",
      "6.2474 % -- latest_one_month_suc\n",
      "6.2474 % -- latest_one_month_fail\n",
      "6.2474 % -- loans_long_time\n",
      "6.2474 % -- loans_credit_limit\n",
      "6.2474 % -- loans_credibility_limit\n",
      "6.2474 % -- loans_org_count_current\n",
      "6.2474 % -- loans_product_count\n",
      "6.2474 % -- loans_max_limit\n",
      "6.2474 % -- loans_avg_limit\n",
      "6.2474 % -- consfin_credit_limit\n",
      "6.2474 % -- consfin_credibility\n",
      "6.2474 % -- consfin_org_count_current\n",
      "6.2474 % -- consfin_product_count\n",
      "6.2474 % -- consfin_max_limit\n",
      "6.2474 % -- consfin_avg_limit\n",
      "6.3946 % -- latest_query_day\n",
      "6.2474 % -- loans_latest_day\n"
     ]
    }
   ],
   "source": [
    "for feature in data.columns:\n",
    "    summ = data[feature].isnull().sum()\n",
    "    if summ:\n",
    "        print('%.4f'%(summ*100/4754), '%',  '--', feature)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) student_feature 缺失占比多, 需要用**众数**填充;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1754\n",
       "2.0       2\n",
       "Name: student_feature, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['student_feature'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data['student_feature'].fillna(1.0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    4752\n",
       "2.0       2\n",
       "Name: student_feature, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['student_feature'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) 其余特征用**均值**填充。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for feature in data.columns:\n",
    "    summ = data[feature].isnull().sum()\n",
    "    if summ:\n",
    "        data[feature].fillna(data[feature].mean(), inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "'Unnamed: 0', 'custid', 'trade_no', 'bank_card_no', 'source'都没用, 'id_name'表示姓名, 没用？\n",
    "\n",
    "'take_amount_in_later_12_month_highest'数值跨度可能较大, 归一化？\n",
    "\n",
    "某些特征可能包含缺失值NAN\n",
    "\n",
    "'reg_preference_for_trad'需要编码\n",
    "\n",
    "'latest_query_time', 'loans_latest_time'为日期特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 模型选择"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据集划分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features = [x for x in data.columns if x not in ['status']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "# 划分训练集测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "X = data[features]\n",
    "y = data.status\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,random_state=2333)\n",
    "\n",
    "# 特征归一化\n",
    "std = StandardScaler()\n",
    "X_train = std.fit_transform(X_train)\n",
    "X_test = std.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LR模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "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": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVM模型\n",
    "\n",
    "> 线性核函数、多项式核函数、高斯核函数、sigmoid核函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "\n",
    "svm_linear = svm.SVC(kernel = 'linear', probability=True).fit(X_train, y_train)\n",
    "svm_poly = svm.SVC(kernel = 'poly', probability=True).fit(X_train, y_train)\n",
    "svm_rbf = svm.SVC(probability=True).fit(X_train, y_train)\n",
    "svm_sigmoid = svm.SVC(kernel = 'sigmoid',probability=True).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 决策树模型\n",
    "\n",
    "> 树模型，特征不需归一化。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier(max_depth=4)\n",
    "dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### XGBoost模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.1, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=100,\n",
       "       n_jobs=1, nthread=None, objective='binary:logistic', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
       "       silent=True, subsample=1)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from xgboost.sklearn import XGBClassifier\n",
    "\n",
    "xgb = XGBClassifier()\n",
    "xgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LightGBM模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
       "        importance_type='split', learning_rate=0.1, max_depth=-1,\n",
       "        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
       "        n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
       "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
       "        subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from lightgbm.sklearn import LGBMClassifier\n",
    "\n",
    "lgb= LGBMClassifier()\n",
    "lgb.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型调参"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "lr = LogisticRegression()\n",
    "lr_param = {'penalty': ('l1', 'l2'), 'C': [0.0001,0.001,0.01,0.1,1,10,100,1000]}\n",
    "\n",
    "grid = GridSearchCV(lr, param_grid = lr_param, cv = 5)\n",
    "grid.fit(X_train, y_train)\n",
    "\n",
    "print(grid.best_params_, grid.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 模型评估\n",
    "\n",
    "> 观察accuracy、precision，recall和f1-score、auc的取值，并画出roc曲线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import roc_auc_score,roc_curve, auc\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def model_metrics(clf, X_train, X_test, y_train, y_test):\n",
    "    # 预测\n",
    "    y_train_pred = clf.predict(X_train)\n",
    "    y_test_pred = clf.predict(X_test)\n",
    "    \n",
    "    y_train_proba = clf.predict_proba(X_train)[:,1]\n",
    "    y_test_proba = clf.predict_proba(X_test)[:,1]\n",
    "    \n",
    "    # 准确率\n",
    "    print('[准确率]', end = ' ')\n",
    "    print('训练集：', '%.4f'%accuracy_score(y_train, y_train_pred), end = ' ')\n",
    "    print('测试集：', '%.4f'%accuracy_score(y_test, y_test_pred))\n",
    "    \n",
    "    # 精准率\n",
    "    print('[精准率]', end = ' ')\n",
    "    print('训练集：', '%.4f'%precision_score(y_train, y_train_pred), end = ' ')\n",
    "    print('测试集：', '%.4f'%precision_score(y_test, y_test_pred))\n",
    "\n",
    "    # 召回率\n",
    "    print('[召回率]', end = ' ')\n",
    "    print('训练集：', '%.4f'%recall_score(y_train, y_train_pred), end = ' ')\n",
    "    print('测试集：', '%.4f'%recall_score(y_test, y_test_pred))\n",
    "    \n",
    "    # f1-score\n",
    "    print('[f1-score]', end = ' ')\n",
    "    print('训练集：', '%.4f'%f1_score(y_train, y_train_pred), end = ' ')\n",
    "    print('测试集：', '%.4f'%f1_score(y_test, y_test_pred))\n",
    "    \n",
    "    # auc取值：用roc_auc_score或auc\n",
    "    print('[auc值]', end = ' ')\n",
    "    print('训练集：', '%.4f'%roc_auc_score(y_train, y_train_proba), end = ' ')\n",
    "    print('测试集：', '%.4f'%roc_auc_score(y_test, y_test_proba))\n",
    "    \n",
    "    # roc曲线\n",
    "    fpr_train, tpr_train, thresholds_train = roc_curve(y_train, y_train_proba, pos_label = 1)\n",
    "    fpr_test, tpr_test, thresholds_test = roc_curve(y_test, y_test_proba, pos_label = 1)\n",
    "    \n",
    "    label = [\"Train - AUC:{:.4f}\".format(auc(fpr_train, tpr_train)), \n",
    "             \"Test - AUC:{:.4f}\".format(auc(fpr_test, tpr_test))]\n",
    "    plt.plot(fpr_train,tpr_train)\n",
    "    plt.plot(fpr_test,tpr_test)\n",
    "    plt.plot([0, 1], [0, 1], 'd--')\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.legend(label, loc = 4)\n",
    "    plt.title(\"ROC curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.7995 测试集： 0.8024\n",
      "[精准率] 训练集： 0.7094 测试集： 0.7052\n",
      "[召回率] 训练集： 0.3488 测试集： 0.3456\n",
      "[f1-score] 训练集： 0.4677 测试集： 0.4639\n",
      "[auc值] 训练集： 0.8054 测试集： 0.8050\n"
     ]
    },
    {
     "data": {
      "image/png": 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FYfsp4DdXBuV18iWE2Tm9mKBjCF4hIzuDf2/4N4Iwsv1ILWCnPF5xYwo7gZ1Y\nVVHVxXJICOMYxo6IW5nQpqF2GXmBpANJjF01lt0ndzPo8kFawE55heK6j342xvQQkWOA4yOpgrU+\nTqjLo/MGDmsj7Ii4VbuMvMDpzNO89d+3mLttLuHB4Xxwwwd0qt/J3WEpVSaK6z7KW3JTV/a4WLa5\nCKvznjLSLiOvcOjcIRamLOSemHt4pM0jWq9IeZXiuo/yZjE3AvYZYzJFpDtwFfApVmE8VRSHbqOF\nOV31sVMPdyzjGEt3LWVw88FE14hmyYAluhKa8krOVEn9CmspzibAx1hF8Wa5NCpv4FDCYkfEQE0I\nHsoYw3c7vyNuYRwT105k14ldAJoQlNdyJinkGmOygFuBt4wxfwe0H6Q4jiUsDnZ2dzTqIh06e4jH\nfnyMJ1c+Sf1q9Zl781wtUaG8nlPLcYrIQOBuIM62LcB1IXkBWythYY5VYVxLWHienNwc7vvuPg6d\nPcSo2FHc2eJOLWCnfIKzM5ofxiqdvUNEooDZrg3LQ9nWRuDAJmjcnRWZfegUhXYdeZB9p/dRt2pd\n/P38ea7Tc4SHhBNRXf/+lO9wZjnOzcBjQJKINAf2GGNecnlknigvIdRrRWLwtSTuTHd3RMpJObk5\nfLTlI/p91Y+526xakF0bdtWEoHyOMyuvXQ18AuzFmqNQT0TuNsb86urgPFK9VsyKede+1rJ2HVV8\nfxz7gzEJY9h0ZBM9wntwbcS17g5JKbdxpvvoTaCPMSYZQERaYCWJWFcG5nFsg8s07s7C9XsB9DFU\nD/D5ts95ec3LhASEMPHqidwUdZPOSlY+zZmkEJiXEACMMVtFJNCFMXkm2+ByYvC1JK5L10VzKri8\nkhRRNaK4ofENPN3xaUKDdJK+Us4khf+KyHtYrQOAO9GCeBdyaCW8kd4VSNduowrqXPY5pvw2BT8/\nP55o/wQd6nWgQ70O7g5LqQrDmXkKw4HtwFPA08AO4G+uDMqjOMxczhtc1lZCxbT2wFoGLBrAR8kf\ncTbrLMYUWGVWKZ9XbEtBRFoBTYAFxphXyyckD5PXbdQyntvXNQd0cLmiOZV5ijfWvcH8/82nUUgj\npt8wXctbK1WEIlsKIvJPrBIXdwLLReSv5RaVpyjQbaSDyxXR4XOH+XbHt9zX8j6+uOULTQhKFaO4\n7qM7gauMMQOBDsBDpT25iPQWkW0ikiIizxSz320iYkTEs55oyqtvdLwdyftPardRBZKekc5nWz8D\nILpGNN9PRAEdAAAgAElEQVQN+I5/xP6DKpWquDkypSq24rqPzhtjzgAYYw6LiDPjD3Yi4o+1Ytv1\nQBqwVkQWOT7JZNsvBGtyXGKpIq8gtgS24l8nuhNTv7p2G1UAxhgW71zMK2te4XTWabo16EZkjUh9\nskgpJxWXFKId1mYWoInjWs3GmFtLOHdHIMUYswNAROYA/YDkfPu9ALwKjCpN4G7lWM6CCGLqV9fF\ncyqAA2cO8MLqF1iZtpKrwq5iXNdxWsBOqVIqLikMyPf6nVKeuyGwx+F1GnDB8lQi0hZoZIz5RkSK\nTAoiMgwYBhARUQG6Z2wJ4WC1Znx6sDXo+uxul52bzdDvhnI04yhPdXiKO5rfgb+fv7vDUsrjFLfI\nzopLPHdh00LtzwDauqPeBO4r6UTGmGnANIDY2Fj3PkfoMLj8WOZzJOak64pqbrT39F7qVa1HJb9K\nxHeJJzwknEYhjdwdllIeq1TjBKWUhrVqW55wYJ/D6xDgSuAnEdkFdAYWVejBZp2TUGFk52Yzc/NM\n+n3Vjznb5gDQpUEXTQhKXSJXFohfCzSzldreCwwG7sh70xhzAof1n0XkJ2CUMSbJhTFdGp2TUCFs\nS9/GmIQxbDm6hWsaXcP1ja93d0hKeQ2nk4KIVDbGnHd2f2NMtog8CiwF/IEPjTFbRGQ8kGSMWVT6\ncN3vYGisPSHonITyN+f3OUxcM5Hqlaszqcckbmx8oxawU6oMOVM6uyMwHagBRIhIa+AB27KcxTLG\nLAYW59sWX8S+PZ0J2J0Onspg55EzgCaE8pZXwK5pzab0jurNUx2eolZQLXeHpZTXcaalMBm4GWt2\nM8aYDSJyjUujqoiSZlA3PYmdtNCEUI7OZp3lX7/9i0p+lfhH7D+IrRdLbL2KO+yklKdzZqDZzxiz\nO9+2HFcEU5EdTPgUgI21rteEUE5W71/NrYtu5dOtn5KZk6kF7JQqB860FPbYupCMbZby34H/uTas\nimVWYirRR86wkxYEd33Q3eF4vZOZJ3k96XW+/ONLGldvzMzeM2lft727w1LKJziTFB7C6kKKAA4C\n33MRdZA82cL1exkJRIVVo7O2Elzu6LmjLNm5hL9e+Vceav0QQZWC3B2SUj6jxKRgjDmE9TipT0qc\n9zoj986jlX8q1ULaujscr3Xk3BG+2/kdd8XcRVSNKJYOWKoDyUq5gTNPH72Pw0zkPMaYYS6JqAKZ\nlZhK9KZ5xMhuTteKoVqr29wdktcxxvDNjm+YuHYiZ7POcnX41TSu3lgTglJu4kz30fcOPwcB/bmw\nppFXmpWYyqZFb3FHwFYOhsZS97FLrfqh8tt/ej/jV4/nP3v/Q+vLWjO+63gaV2/s7rCU8mnOdB/N\ndXwtIp8Ay10WUQWxcP1eRvonAFC3611ujsb7ZOdmM3TpUNIz0nmm4zMMvmKwFrBTqgK4mDIXUYDX\nf53rdXYxnf22QuPuEDvU3eF4jT2n9tCgWgMq+VVibNexNAppRMNgLRWiVEVR4jwFETkmIum2P8ex\nWgn/dH1o7jMrMZWrjtkaQzqOUCayc7OZvmk6cV/F2QvYda7fWROCUhVMsS0FsYrKtMYqaAeQa3xg\nBtHphPfp7GcbS9BWwiX7Pf134n+NZ2v6VnpF9OKGxje4OySlVBGKTQrGGCMiC4wxvjNzKGkGw05M\nBnQsoSzM2jqLSWsnUaNyDd7o+YZWNFWqgnOmzMUaEWnn8kgqClt57Gk1HtOxhEuQ16C8vNbl9Inu\nw8K4hZoQlPIARbYURKSSMSYb6A48KCLbgTNYK6oZY4z3JQrbqmqrc1uwomofvH4ihguczTrL5N8m\nU0kqMarDKC1gp5SHKa77aA3QDogrp1jcz9ZKWJjTVRfPuQgJexMYt2oc+8/s544Wd9jLXSulPEdx\nSUEAjDHbyykW97K1ErYEtmJH/YG8rDWOnHbi/AkmrZ3Ewu0LiaweyczeM2lX1/sakkr5guKSwmUi\n8kRRbxpj3nBBPO7hsPbyr1V8b6mIS5Wekc7y3ct5oNUDDG89nMr+ld0dklLqIhWXFPyBYGwtBq/m\nsPbyhHXN6RTl5ng8wJFzR1i8YzH3tLzHXsCuZlBNd4ellLpExSWF/caY8eUWibs17s4b6V2BdB1P\nKIYxhkXbF/Hq2lfJyM6gR6MeNK7eWBOCUl6ixDEFX3HwVAaJ+9LpFBWqK6sVYe/pvYxfNZ6EfQm0\nrdOWsV3HagE7pbxMcUmhV7lFUQEcOX0eQFsJRcjOzeb+pfdzLOMYozuNZtAVg/ATZ6a5KKU8SZFJ\nwRiTXp6BuI3tqSMCW2kroRCpJ1NpGNyQSn6VGN91POEh4TQIbuDusJRSLqJf9WyDzPrU0YWycrN4\nf+P7xC38s4Bdx/odNSEo5eUupnS21zkYGsuEfZ31qSOb5KPJjEkYw+/pv3ND4xu4MfJGd4eklCon\nmhTQ8QRHn239jElrJ1ErqBZv9XyLXo19amhJKZ+nScHG18cT8kpSNA9tTt8mfRkVO4oalWu4Oyyl\nVDnz+aRw8FQGpzKy3R2G25zJOsNb694i0D+QJzs8Sfu67Wlf13cqpSulLuTbA81JM6ibngT4ZtfR\nf/b+h/4L+zN321wMBh9YP0kpVQLfbinYnjzaWOt6hvlQ19HxjONMSprEou2LiK4Rzcc3fUybOm3c\nHZZSqgLw7ZYCsCWwFSuq9nF3GOXq+PnjrEhdwd+u+hvz+s7ThKCUsnNpUhCR3iKyTURSROSZQt5/\nQkSSRWSjiKwQEa2Z4CKHzx5m5uaZGGOIrBHJ0gFLebTtowT6B7o7NKVUBeKypCAi/sAU4CYgBhgi\nIjH5dvsNiDXGXAXMB151VTyF8YVBZmMMC/5YQL+v+vHO+ndIPZUKoE8WKaUK5cqWQkcgxRizwxiT\nCcwB+jnuYIz50Rhz1vZyNRDuwngK8Pb5CWmn0hi2fBjxCfFcHno58/vO1wJ2SqliuXKguSGwx+F1\nGtCpmP3vB5YU9oaIDANryeSIiLIdEA4JquSV8xOyc7N5YNkDHD9/nOc7P89tl9+mBeyUUiVyZVIo\nrPR2oc88ishdQCzQo7D3jTHTgGkAsbGx+txkMXaf3E14cDiV/CrxQrcXaBTSiHrV6rk7LKWUh3Dl\nV8c0oJHD63BgX/6dROQ6YDRwizHmvAvjuUDivNdpmbmpvC7nclm5Wby34T36L+zP7N9nA9ChXgdN\nCEqpUnFlS2Et0ExEooC9wGDgDscdRKQt8B7Q2xhzyIWxXChpBp22WIvKnW7Wv9wu6ypbjmwhPiGe\n/x37HzdF3sRNUTe5OySllIdyWVIwxmSLyKPAUqz1nj80xmwRkfFAkjFmETAJax3oeSICkGqMucVV\nMdnZJq1Nq/EYwwb+w+WXc6VPkz9lUtIkwoLCmHzNZK6J0BLgSqmL59IZzcaYxcDifNviHX6+zpXX\nL07epLVh7grgEuUVsGsZ1pL+TfvzROwTVA+s7u6wlFIezvfKXNhWWjuV28LdkVyU05mneXPdmwT6\nB/J0x6dpW6ctbeu0dXdYSikv4XvPKNq6jhbmdPW4+Qkr01YStzCO+X/Mp5JfJS1gp5Qqc77XUsDq\nOtpRfyAve8j8hGMZx5i4diLf7viWpjWb8kbPN7jqsqvcHZZSygv5ZFLwNCczT/Lznp95qPVDPNjq\nQQL8A9wdklLKS2lSqKAOnjnItzu/ZWjLoTSu3pilty3VgWSllMv51piCbZC5IjPGMP9/84lbGMe7\n699lzymrUogmBKVUefCtloJtkPnTMx2htptjKcSek3sYu2osaw6soUO9DoztMpaI6p4x7qEqhqys\nLNLS0sjIyHB3KMpNgoKCCA8PJyDg4rqZfSspYA0yz87oxYQK9uRRXgG7E5kniO8Sz4BmA7SAnSq1\ntLQ0QkJCiIyMxDYhVPkQYwxHjx4lLS2NqKioizqHzyUFgE5RoRWmMurOEztpFNKISn6VeLH7i1rA\nTl2SjIwMTQg+TESoXbs2hw8fvuhz6FdRN8nKyeLd9e9y66JbmfP7HEAL2KmyoQnBt13q379PthTc\nbdPhTcQnxJNyPIU+UX34v+j/c3dISikFaEuh3H2S/Al3LbmLk5kneefad5j4l4nUCqrl7rCUumRH\njx6lTZs2tGnThnr16tGwYUP768zMTKfOMXToULZt21ZmMWVmZhIaGsrzzz9/wfbw8HCOHz9uf/39\n998TFxdnf/3tt9/Svn17YmJiaN68OU8//XSh53/xxRdp2rQpzZs35/vvvy90n2XLltG2bVvatGnD\n1VdfzY4dOwCrq++2226jadOmdOnShdRUa6nclJQUqlSpYv/dPfLIIwXO2adPH9q0aVO6X4aTtKVQ\nTvIK2LUKa8WAZgMY2X4kIYEh7g5LqTJTu3Zt1q9fD8DYsWMJDg5m1KhRF+xjjMEYg59f4d9HZ8yY\nUaYxfffdd8TExDB37lxeeOEFp47ZsGEDI0aM4Ntvv+Xyyy8nOzub999/v8B+Gzdu5MsvvyQ5OZk9\ne/bQu3dvtm3bVuDehg8fztKlS2nWrBmTJ09mwoQJfPDBB0ybNo169eqRkpLCp59+yrPPPstnn30G\nwBVXXGH/Xeb3+eefU7NmTfbtK7A8TZnQpOBipzJP8ca6NwjyD+Lpjk/Tpk4b2tRxTYZXytG4r7eQ\nvO9kmZ4zpkF1xvRtWapjUlJSiIuLo3v37iQmJvLNN98wbtw4/vvf/3Lu3Dluv/124uOt4sndu3fn\nnXfe4corryQsLIzhw4ezZMkSqlatysKFC6lTp06prj179myeeOIJ3nzzTdauXUuHDh1KPGbixIk8\n//zzXH755QBUqlSJhx56qMB+CxcuZMiQIQQGBtKkSRMiIiJYt25dgWuICCdPWn8PJ06coEGDBvbj\nX3nlFQAGDRrEiBEjSozt5MmTTJ48mXfffZe77767xP0vhnYfudBPe34i7qs4vvzjSwL8A7SAnfJZ\nycnJ3H///fz22280bNiQV155haSkJDZs2MDy5ctJTk4ucMyJEyfo0aMHGzZsoEuXLnz44YeluuaZ\nM2f4+eef6dOnD0OGDGH27NlOHbd582bat29f6HsLFixg/Hhrga69e/fSqNGfi0uGh4ezd+/eAsdM\nnz6dG264gfDwcObOnctTTz1V4PjAwECqVatm79JKSUmhbdu29OzZk4SEBPu5Ro8ezdNPP02VKlWc\nupeLoS0FF0jPSOeVNa+wZOcSmtVqxtvXvs2VYVe6OyzlY0r7jd6VmjRpcsE36NmzZzN9+nSys7PZ\nt28fycnJxMTEXHBMlSpVuOkmaxXB9u3b88svv5TqmosWLeL6668nKCiIgQMHEhsby2uvvYafn1+h\nT+g489RO//796d/fWq2xsC95hZ3jzTffZOnSpcTGxvLyyy8zatQopk6dWuTx4eHhpKamEhoaypo1\naxgwYABbt25l27ZtpKWl0bdvX1JSUpz5FVwUn2opHDyVwamMbJdf53Tmaf6T9h8ebvMwc/9vriYE\n5fOqVatm//mPP/7g7bff5ocffmDjxo307t270BnYgYGB9p/9/f3Jzi74/+51111HmzZtGD58eIH3\nZs+ezXfffUdkZCQdOnTg0KFDrFy5ErDGP44dO2bfNz09nbCwMABatmzJunXrSryn8PBw9uzZY3+d\nlpZm7xrKs3//fn7//XdiY2MBuP322+3f/B2Pz8zM5MyZM9SoUYOgoCBCQ0MB6NixI40bNyYlJYVV\nq1aRmJhIZGQkPXv2JDk5mV69epUYZ2n5VFI4cvo8gEvWUThw5gAfbPoAYwwR1SNYettSHmr9kFY0\nVSqfkydPEhISQvXq1dm/fz9Lly696HN9//33rF+/nqlTp16w/dixYyQmJpKWlsauXbvYtWsXkydP\ntnch9ezZk08++QSA7OxsPvvsM665xlrK9qmnnuLFF1+0fxvPycnhjTfeKHDtW265hdmzZ5OZmcn2\n7dvZvXt3gW6n2rVrc+TIEfu5li9fTosWLezHf/TRR4A1eHzDDTcAcPjwYXJycgCrG2nHjh1ERUXx\n6KOPsm/fPnbt2sVPP/1ETEwMK1asuOjfXVF8rvsoJKhSmc5mzjW5zP/ffN5Y9wa5JpcbGt9ARPUI\nfbJIqSK0a9eOmJgYrrzySqKjo+nWrVuZX+OLL77g+uuvv6D+T1xcHKNHj+add95h7NixDB8+nNat\nW2OMsY87ALRt25bXXnuNQYMGce7cOUSEfv36AdaYwqZNm4iPj6d169bExcXRokULKlWqxL///W/7\nk0c33ngjn3zyCXXq1GHatGnExcXh7+9PaGio/QmrYcOGcdddd9G0aVPCwsKYM8eaxPrjjz8ybtw4\nAgIC8Pf35/3336dGjRpl/jsqinja4GdsbKxJSkq6qGO3TOgOQMt/lk2l1N0ndzM2YSxJB5PoVL8T\nY7qMoVFIo5IPVMpFtm7dav8mqnxXYf8ORGSdMSa2pGN9rqVQVrJzsxm2bBinMk8xvut44prGaXkB\npZTH06RQSjuO7yCiegSV/Cox4eoJNAppRJ2qpXt2WimlKiqfGmi+FJk5mUxZP4UBiwYw+3drsKp9\n3faaEJRSXkVbCk7YcHgDY34dw/YT2+kb3Ze+0X3dHZJSSrmEJoUSfLTlI15Pep261ery717/5urw\nq90dklJKuYwmhSLkmlz8xI/Wl7Vm0BWDGNFuBMGBwe4OSymlXMp3xhSSZtAyc1OJu53MPEn8r/G8\nssYqVNWmThue6/ycJgSlSlAWpbMBPvzwQw4cOHBRMaxduxYRuWBSV0pKSoEy08899xxvvfUWYJWr\nePXVV7niiiu48soradOmjb1aqaOiSl3n99prr9GyZUuuvPJK7rzzTs6ftybNbt++nY4dO9K0aVPu\nuOMOsrKyAPjggw+47LLL7L8rx0qx06dPp1mzZjRr1oxPP/30on4npeUzSeFggvUL/bXKNUXusyJ1\nBXFfxbFo+yKqBVTTAnZKlUJe6ez169czfPhwRo4caX/tWLKiJJeSFGbPnk337t2dLn4HMGXKFH78\n8UeSkpLYvHkzP/30k31GsSPHUtePPPIIzz77bIF9du/ezdSpU1m3bh2bNm0iIyODefPmAfDkk0/y\n1FNPkZKSQtWqVZk5c6b9uDvvvNP+uxo6dCgAR44cYcKECaxdu5bVq1fz/PPPc+LEiVL+RkrPZ7qP\njpw+z87cFgR3fbDAe0fPHWVC4gSW7V5G89DmvNPrHWJqxxRyFqU8yJJn4EDJreNSqdcKbnql1Id9\n9NFHTJkyhczMTLp27co777xDbm4uQ4cOZf369RhjGDZsGHXr1mX9+vXcfvvtVKlShTVr1jidUHJz\nc/niiy/48ccfufrqq8nMzHTq2AkTJrBq1SpCQqwqBDVr1uSee+4psJ+zpa6zsrLIyMjA39+fs2fP\n0qBBA3Jycli5cqU9Qdx777288sorPPhgwc+jPEuWLKF3797UrFkTgGuvvZZly5YxcODAEu/pUvhM\nSwGKLnFxJusMq/av4rG2jzHr/2ZpQlCqDG3evJkFCxaQkJDA+vXryc7OZs6cOaxbt44jR46wadMm\nNm/ezD333MPtt99OmzZtmDt3bqlbGCtXrqR58+b20hnfffddicccO3aMrKwsGjduXOj7o0ePZvHi\nxUDxpa7zNG7cmMcff5xGjRpRv3596tSpw7XXXsvhw4cJCwvD398fKFhm+/PPP+eqq65i0KBB9u3O\nluYuaz7TUthTKYe3Q8/yr2MpNK3VlP2n9/P1jq95sNWDRFSPYPlty6kWUK3kEynlKS7iG70rfP/9\n96xdu9ZeKfTcuXM0atSIG2+8kW3btvH444/Tp08fe0G4izV79mwGDx4MwODBg5k9eza33HJLkZUG\nRKTELuKXXnrJ/rMzpbKPHj3KN998w86dO6levToDBgxgzpw59OjRo8hj4+LiuPvuu6lcuTJTpkxh\n6NChLFu2zL5aY3HXcwWXthREpLeIbBORFBF5ppD3K4vIXNv7iSIS6Yo4zmadZWLtM+ytlMvDKx7m\n4y0fE7cwjg82fcCeU1bpWk0ISrmGMYa//vWv9j7zbdu28fzzz1O7dm02btxI9+7dmTx5Mn/729+K\nPc+uXbvsg7EffPDBBe9lZWWxYMEC4uPjiYyMtC+neebMmQJlsuHPUtmhoaEEBAQUOWjsqKhS146W\nLVtGs2bNCAsLIzAwkP79+5OQkECdOnU4cuSIfazCscx2WFgYlStXBqwieWvXri1wvfzHuJLLkoKI\n+ANTgJuAGGCIiOTvl7kfOGaMaQq8CUx0RSzxCfGc8DMYsUpcT0qaROvLWrOg3wIiqpddxVSlVEHX\nXXcdn3/+OUeOHAGsb9OpqakcPnwYYwwDBw60L88JEBISwqlTpwqcJzIy0p5YHnjggQveW7ZsGR06\ndGDPnj3s2rWL1NRU+vbty6JFi6hZsya1atXi559/tl9/2bJl9uqszzzzDA8//LD9msePHy90Teai\nSl07ioiIYNWqVZw7dw5jDCtWrKBFixb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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd69c3fe10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 逻辑回归\n",
    "model_metrics(lr, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.7908 测试集： 0.7954\n",
      "[精准率] 训练集： 0.7647 测试集： 0.7905\n",
      "[召回率] 训练集： 0.2476 测试集： 0.2351\n",
      "[f1-score] 训练集： 0.3741 测试集： 0.3624\n",
      "[auc值] 训练集： 0.8042 测试集： 0.8094\n"
     ]
    },
    {
     "data": {
      "image/png": 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t0ISVpRvSmb9/Pl8f/RrvGt580OcDuvl2s3ZYopIoKikEKaWuVkJVQMN8y2it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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd6bcd8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 线性SVM\n",
    "model_metrics(svm_linear, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.8284 测试集： 0.7554\n",
      "[精准率] 训练集： 0.9786 测试集： 0.5208\n",
      "[召回率] 训练集： 0.3274 测试集： 0.1416\n",
      "[f1-score] 训练集： 0.4906 测试集： 0.2227\n",
      "[auc值] 训练集： 0.9391 测试集： 0.7117\n"
     ]
    },
    {
     "data": {
      "image/png": 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Rxa/PKbieOVhQZN1Yt9fpQF9/xuBrNsAcBYq2DKzYXMDMzZjLgyseJEZiuO+s+xjWbpgV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\n1qCqvPPOO2zf/tvU9W+//TZ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PIpIJLAD+GpjQgqas/7+XSSTOp+DpG3/R+2692SaceH0+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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0aea39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 多项式SVM\n",
    "model_metrics(svm_poly, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.8266 测试集： 0.7975\n",
      "[精准率] 训练集： 0.9046 测试集： 0.7963\n",
      "[召回率] 训练集： 0.3500 测试集： 0.2436\n",
      "[f1-score] 训练集： 0.5047 测试集： 0.3731\n",
      "[auc值] 训练集： 0.9170 测试集： 0.7680\n"
     ]
    },
    {
     "data": {
      "image/png": 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NSZSLep716BXQi1e6vkLtqrVtHY6wQ8WNaH4LGAHsw2hcXo5RIXU6MM464dmH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\nVVkul5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0af11cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 高斯核SVM\n",
    "model_metrics(svm_rbf, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.7205 测试集： 0.7379\n",
      "[精准率] 训练集： 0.4373 测试集： 0.4662\n",
      "[召回率] 训练集： 0.3738 测试集： 0.4108\n",
      "[f1-score] 训练集： 0.4031 测试集： 0.4367\n",
      "[auc值] 训练集： 0.6600 测试集： 0.6784\n"
     ]
    },
    {
     "data": {
      "image/png": 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8RO2KtR0djnACtiSFxzB1IfkDZ4EN5n1CFKyAhXBezHyYKEMv6npVYOPYjpIQ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\nUXDOOZf2f5Tk65xnPVXUAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0afbc208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sigmoid-SVM\n",
    "model_metrics(svm_sigmoid, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.7920 测试集： 0.7737\n",
      "[精准率] 训练集： 0.6581 测试集： 0.5862\n",
      "[召回率] 训练集： 0.3667 测试集： 0.2890\n",
      "[f1-score] 训练集： 0.4709 测试集： 0.3871\n",
      "[auc值] 训练集： 0.7727 测试集： 0.7468\n"
     ]
    },
    {
     "data": {
      "image/png": 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XlsfqY6uZEjmFjj4d+XHyj1KvqImoMSlorXcBu5RSX2itS2wYk2gg4k7m8dSP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\nMAzDMDyYUTAMwzA8/D/1XYBPOLcivgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0b02d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 决策树\n",
    "model_metrics(dt, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.8521 测试集： 0.8045\n",
      "[精准率] 训练集： 0.8718 测试集： 0.7079\n",
      "[召回率] 训练集： 0.4857 测试集： 0.3569\n",
      "[f1-score] 训练集： 0.6239 测试集： 0.4746\n",
      "[auc值] 训练集： 0.9166 测试集： 0.7972\n"
     ]
    },
    {
     "data": {
      "image/png": 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XfwVWiEh2O7D5rYyZRwXiOsvIEoLPO3L+CIv3LOaO5nfwSf9PLCEYk4vcuo/+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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0b0cb0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# XGBoost\n",
    "model_metrics(xgb, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[准确率] 训练集： 0.9949 测试集： 0.7961\n",
      "[精准率] 训练集： 1.0000 测试集： 0.6550\n",
      "[召回率] 训练集： 0.9798 测试集： 0.3711\n",
      "[f1-score] 训练集： 0.9898 测试集： 0.4738\n",
      "[auc值] 训练集： 1.0000 测试集： 0.7869\n"
     ]
    },
    {
     "data": {
      "image/png": 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xpqJlrQSf2H5kO+NXj2dr8laujLiSqxte7euQjAkYHicFESmrqmc93V9VM0Tk\nXmAZEAq8rapbRWQSEK+qCwsfrgl2c36ew9T1U6lctjLP9niWaxteawXsjClCnpTO7gS8BVQBIkWk\nLXCnqt5X0HtVdTGwOMe2cXns29OTgE1wyi5g17RqU/o06sPDHR+mWlg1X4dlTMDxpKUwDfgLMB9A\nVTeKyJVejaqks6moxeZ0+mle+ekVSoWU4h8x/yCmbgwxdf1j2MkYf+TJQHOIqu7OsS3TG8H4DRtk\nLhZr96/l+oXX8/6290nLTLMCdsYUA09aCnudXUjqvEv5PuAX74blB2yQ2WtOpJ3g+fjn+WzHZzSs\n3JB3+rxDhzodfB2WMUHBk6RwN44upEjgf8AKLqAOkjGeSj6TzJLflvC31n/j7rZ3E1YqzNchGRM0\nCkwKqnoQx3RSY7zm8JnDLP1tKTe3vJlGVRqxbNAyG0g2xgc8mX30H9zuRM6mqiO8ElFJZ4PMRUpV\n+WLnF0yNm8rp9NNcEX4FDSs3tIRgjI940n20wu1xGDCQc2saBY/4mfDFaMdjG2S+aPtP7WfS2kl8\nt+872tZqy6Suk2hYuaGvwzImqHnSfTTX/bmIvAcs91pEJVn2rKO/vGSDzBcpIyuD4cuGcyT1CI92\nepRhlw6zAnbGlAAXUuaiERB8X+esAF6R2HtyL/Ur1KdUSCkmdJ1ARKUIGlRs4OuwjDFOBd6nICJH\nReSI8+cYjlbCv7wfWglj9yZclIysDN7a/Bax82NdBewur3e5JQRjSph8WwriKCrTFkdBO4AsDcY7\niKyVcFF+PvIz474fx7Yj2+gV2YtrGl7j65CMMXnINymoqorIPFUN3juHbHD5ony47UOejXuWKmWr\n8ELPF6yiqTElnCdjCutFpL2q/uj1aEoiG1y+INkF7C6pdgn9Gvfj4Y4PU6VsFV+HZYwpQJ5JQURK\nqWoG0B24S0R+BVJwrKimqtq+mGL0Hes2KrTT6aeZ9tM0SkkpHur4kBWwM8bP5NdSWA+0B2KLKZaS\nxwaXC2X1vtVMXDOR/Sn7uanFTa7WgjHGf+SXFARAVX8tplhKFmsleOz42eM8G/csC35dQFTlKN7p\n8w7t6wR+Q9KYQJRfUqglIg/m9aKqvuCFeEoOayV47EjqEZbvXs6dbe5kZNuRlA0t6+uQjDEXKL+k\nEApUxNliCErWSsjT4TOHWbxzMbe2utVVwK5qWFVfh2WMuUj5JYX9qjqp2CIxfkFVWfjrQp6Je4bU\njFR6RPSle3c9AAAcYklEQVSgYeWGlhCMCRD53dEcvC2E7PEEc459p/YxcsVInvj+CZpUbcLH131s\nBeyMCTD5tRR6FVsUJYndrJarjKwM7lh2B0dTj/J458cZcukQQsST1VyNMf4kz6SgqkeKM5ASw25W\nO8eeE3toULEBpUJKManrJMIrhVO/Yn1fh2WM8RL7qufOpqG6pGel859N/yF2wR8F7DrV62QJwZgA\ndyGlswOXTUMFICE5gfGrx/PzkZ+5puE1XBt1ra9DMsYUE0sK2ayVAMAH2z7g2bhnqRZWjZd6vkSv\nhsE5tGRMsLKkkC3IWwnZJSmaV29O/yb9eSjmIStgZ0wQsqTgLghbCSnpKbz0w0uUCS3DPzv+kw51\nOtChTvBWSjcm2NlAMwTtfQnf7fuOgQsGMnf7XBQlGNdPMsacy1oKEHRdR8dSj/Fs/LMs/HUhjas0\n5t2+7xJdO9rXYRljSgBLCtmCqOvo2NljrNyzkr9f9ndGXDaCMqFlfB2SMaaE8Gr3kYj0EZHtIpIo\nIo/m8vqDIpIgIptEZKWIWM0ELzl0+hDvbHkHVSWqShTLBi3j3nb3WkIwxpzDa0lBREKBGUBfoCVw\no4i0zLHbT0CMql4GfAI84614gpWqMm/HPAbMH8D0DdPZc3IPgM0sMsbkypsthU5AoqruVNU0YA4w\nwH0HVf1KVU87n64Fwr0YT9BJOpnEiOUjGLd6HJdUv4RP+n9iBeyMMfny5phCA2Cv2/MkoHM++98B\nLMntBREZAYwAiIyMLKr4AlpGVgZ3fnknx84eY+zlYxl8yWArYGeMKZA3k0JupbdznfMoIjcDMUCP\n3F5X1TeANwBiYmJs3mQ+dp/YTXjFcEqFlOLJbk8SUSmCuhXq+josY4yf8OZXxyQgwu15OPB7zp1E\npDfwOHCdqp71Yjy5C5B7FNKz0nl94+sMXDCQ2T/PBqBj3Y6WEIwxheLNlkIc0ExEGgH7gGHATe47\niEg74HWgj6oe9GIseQuAexS2Ht7KuNXj+OXoL/SN6kvfRn19HZIxxk95LSmoaoaI3Assw7He89uq\nulVEJgHxqroQeBbHOtAfiwjAHlW9zlsx5cmP71F4P+F9no1/lpphNZl25TSujLzS1yEZY/yYV29e\nU9XFwOIc28a5Pe7tzfMHsuwCdq1qtmJg04E8GPMglctU9nVYxhg/F9x3NLuXy/YTp9JO8eIPL1Im\ntAyPdHqEdrXb0a52O1+HZYwJEME9R9HPxhNWJa0idkEsn+z4hFIhpayAnTGmyAVnSyF+piMhHNjs\nF+MJR1OPMjVuKot2LqJp1aa80PMFLqt1ma/DMsYEoOBMCtkJoW4bv2glnEg7wTd7v+HutndzV5u7\nKB1a2tchGWMCVHAmBXAkhOGLfB1Fnv6X8j8W/baI4a2G07ByQ5YNXmYDycYYrwvepFBCqSqf7viU\n5+OfJyMrg96RvYmsHGkJwRhTLCwplCB7T+xlwpoJrD+wno51OzKhywQiK1utJ+O59PR0kpKSSE1N\n9XUoxkfCwsIIDw+ndOkL62a2pFBCZBewO552nHFdxjGo2SArYGcKLSkpiUqVKhEVFYXzhlATRFSV\n5ORkkpKSaNSo0QUdw5KCj/12/DciKkVQKqQUk7tPtgJ25qKkpqZaQghiIkKNGjU4dOjQBR/Dvor6\nSHpmOv/e8G+uX3g9c36eA1gBO1M0LCEEt4v997eWgg9sPrSZcavHkXgskX6N+vHnxn/2dUjGGANY\nS6HYvZfwHjcvuZkTaSeYftV0pv5pKtXCqvk6LGMuWnJyMtHR0URHR1O3bl0aNGjgep6WlubRMYYP\nH8727duLLKa0tDSqV6/O2LFjz9keHh7OsWPHXM9XrFhBbGys6/miRYvo0KEDLVu2pHnz5jzyyCPn\nHfvgwYP07NmTChUqMHr06DxjSE5OplevXjRr1oxrr72W48ePA47+/1GjRtG0aVPatm3Lhg0bXO95\n6623aNasGc2aNeP99993bY+Li6N169Y0bdqUMWPGFP4X4oHgSwo+Wj8huyRFm5ptGNRsEPMHzKdH\nRK5rChnjl2rUqMGGDRvYsGEDI0eOZMyYMa7nZcqUARz/H2RlZeV5jJkzZ3LppZcWWUxLly6lZcuW\nzJ071+P3bNy4kdGjRzN79mwSEhLYsmULUVFR5+1Xvnx5pkyZwtSpU/M93pQpU+jbty87duzgiiuu\n4JlnHEvRf/755+zdu5fExERmzJjBPffcA8Dhw4d56qmniIuLY+3atYwdO9aVSEaOHMnMmTPZsWMH\nW7duZfny5R5fl6eCr/uomOsdnUw7yQs/vEBYaBiPdHqE6NrRRNeOLpZzm+A28fOtJPx+okiP2bJ+\nZcb3b1Wo9yQmJhIbG0v37t1Zt24dX3zxBRMnTuTHH3/kzJkzDB06lHHjHMWTu3fvzvTp02ndujU1\na9Zk5MiRLFmyhPLly7NgwQJq165dqHPPnj2bBx98kBdffJG4uDg6duxY4HumTp3K2LFjueSSSwAo\nVaoUd99993n7VaxYkW7durFt27Z8j7dgwQLWrl0LwG233UafPn2YMmUKCxYs4NZbb3Vd94EDBzh0\n6BBLly6lT58+VK1aFYCrrrqKL7/8kssvv5zU1FTXNdxyyy3Mnz+fq6++2vNfiAeCr6UAxVbv6Ou9\nXxM7P5bPdnxG6dDSVsDOBK2EhATuuOMOfvrpJxo0aMDTTz9NfHw8GzduZPny5SQkJJz3nuPHj9Oj\nRw82btxIly5dePvttwt1zpSUFL755hv69evHjTfeyOzZsz1635YtW+jQoUOur82bN49JkyYVKo7k\n5GRq1aoFQIMGDdi/fz8A+/btIyLij8Upw8PD2bdvX6G3F7Wgain0Or0Y9nu/VPaR1CM8vf5plvy2\nhGbVmvHyVS/TumZrr57TmJwK+43em5o0aXLOt/TZs2fz1ltvkZGRwe+//05CQgItW7Y85z3lypWj\nb1/HKoIdOnTg22+/LdQ5Fy5cyNVXX01YWBg33HADMTExPPfcc4SEhOQ6Q8eTWTsDBw5k4MCBhYoj\nr/Pk9iVRRFxrpeS2Pa9jFaWgail0O/OV44GXu45OpZ3iu6TvGBU9irl/nmsJwQS9ChUquB7v2LGD\nl19+mf/+979s2rSJPn365HoHdvY4BEBoaCgZGRnn7dO7d2+io6MZOXLkea/Nnj2bpUuXEhUVRceO\nHTl48CCrVq0CHOMfR48ede175MgRatasCUCrVq344YcfLvxic3C/b2Dfvn3UreuYdh4eHs7evXtd\n+yUlJVG/fv1Cby9qQZUUAK91HR1IOcCbm99EVYmsHMmywcu4u+3dVtHUmBxOnDhBpUqVqFy5Mvv3\n72fZsmUXfKwVK1awYcMGXnvttXO2Hz16lHXr1pGUlMSuXbvYtWsX06ZNc3Uh9ezZk/feew+AjIwM\nPvjgA6680rGU7cMPP8zkyZNJTEwEIDMzkxdeeOGCY7zuuuuYNWsWALNmzWLAgAGu7e+++y4A3333\nHXXq1KFWrVr06dOHJUuWcOzYMZKTk1m5ciXXXHMNERERlC1blri4OFSV9957z3WsohR8SaGIZWkW\nH23/iNgFsbyx6Q32nnRk8kp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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2bf0b1d8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lightGBM\n",
    "model_metrics(lgb, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 遇到的问题"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1）pd.read_csv读取文件时，utf-8的编码问题。\n",
    "\n",
    "解决方法：reference1\n",
    "\n",
    "2）装完xgboost后在notebook总是显示如下错误：ImportError: cannot import name 'MultiIndex'。\n",
    "\n",
    "**解决方法**：更新scipy和xgboost后还是没有解决；后来重启了一下jupyter就好了....\n",
    "\n",
    "3）画ROC曲线时, tpr取值为nan\n",
    "\n",
    "**解决方法**：注意roc_curve里的几个参数：第二项为**真实y与预测的scores**而不是y_pred，而pos_label=1指在y中标签为1的是标准阳性标签，其余值是阴性。\n",
    "\n",
    "4）svm用函数clf.predict_proba()时候报错如下：\n",
    "\n",
    "> AttributeError: predict_proba is not available when  probability=False\n",
    " \n",
    "**解决方法**：clf = SVC()默认情况probability=False，添加probability=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) [python问题--UnicodeDecodeError: 'utf-8' codec can't decode byte 0xff in position 0: invalid start byte](https://blog.csdn.net/u012429555/article/details/80960985)\n",
    "\n",
    "2) [xgboost的原生版本与sklearn 接口版本对比](https://blog.csdn.net/PIPIXIU/article/details/80463565)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.2"
  },
  "toc": {
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
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