{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-02-29T07:40:36.696135Z",
     "start_time": "2024-02-29T07:40:36.680744Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n0    842302         M        17.99         10.38          122.80     1001.0   \n1    842517         M        20.57         17.77          132.90     1326.0   \n2  84300903         M        19.69         21.25          130.00     1203.0   \n3  84348301         M        11.42         20.38           77.58      386.1   \n4  84358402         M        20.29         14.34          135.10     1297.0   \n\n   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n0          0.11840           0.27760          0.3001              0.14710   \n1          0.08474           0.07864          0.0869              0.07017   \n2          0.10960           0.15990          0.1974              0.12790   \n3          0.14250           0.28390          0.2414              0.10520   \n4          0.10030           0.13280          0.1980              0.10430   \n\n   ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n0  ...         25.38          17.33           184.60      2019.0   \n1  ...         24.99          23.41           158.80      1956.0   \n2  ...         23.57          25.53           152.50      1709.0   \n3  ...         14.91          26.50            98.87       567.7   \n4  ...         22.54          16.67           152.20      1575.0   \n\n   smoothness_worst  compactness_worst  concavity_worst  concave_points_worst  \\\n0            0.1622             0.6656           0.7119                0.2654   \n1            0.1238             0.1866           0.2416                0.1860   \n2            0.1444             0.4245           0.4504                0.2430   \n3            0.2098             0.8663           0.6869                0.2575   \n4            0.1374             0.2050           0.4000                0.1625   \n\n   symmetry_worst  fractal_dimension_worst  \n0          0.4601                  0.11890  \n1          0.2750                  0.08902  \n2          0.3613                  0.08758  \n3          0.6638                  0.17300  \n4          0.2364                  0.07678  \n\n[5 rows x 32 columns]",
      "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>id</th>\n      <th>diagnosis</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave_points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>842302</td>\n      <td>M</td>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.80</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0.27760</td>\n      <td>0.3001</td>\n      <td>0.14710</td>\n      <td>...</td>\n      <td>25.38</td>\n      <td>17.33</td>\n      <td>184.60</td>\n      <td>2019.0</td>\n      <td>0.1622</td>\n      <td>0.6656</td>\n      <td>0.7119</td>\n      <td>0.2654</td>\n      <td>0.4601</td>\n      <td>0.11890</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>842517</td>\n      <td>M</td>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.90</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0.07864</td>\n      <td>0.0869</td>\n      <td>0.07017</td>\n      <td>...</td>\n      <td>24.99</td>\n      <td>23.41</td>\n      <td>158.80</td>\n      <td>1956.0</td>\n      <td>0.1238</td>\n      <td>0.1866</td>\n      <td>0.2416</td>\n      <td>0.1860</td>\n      <td>0.2750</td>\n      <td>0.08902</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>84300903</td>\n      <td>M</td>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.00</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0.15990</td>\n      <td>0.1974</td>\n      <td>0.12790</td>\n      <td>...</td>\n      <td>23.57</td>\n      <td>25.53</td>\n      <td>152.50</td>\n      <td>1709.0</td>\n      <td>0.1444</td>\n      <td>0.4245</td>\n      <td>0.4504</td>\n      <td>0.2430</td>\n      <td>0.3613</td>\n      <td>0.08758</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>84348301</td>\n      <td>M</td>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>77.58</td>\n      <td>386.1</td>\n      <td>0.14250</td>\n      <td>0.28390</td>\n      <td>0.2414</td>\n      <td>0.10520</td>\n      <td>...</td>\n      <td>14.91</td>\n      <td>26.50</td>\n      <td>98.87</td>\n      <td>567.7</td>\n      <td>0.2098</td>\n      <td>0.8663</td>\n      <td>0.6869</td>\n      <td>0.2575</td>\n      <td>0.6638</td>\n      <td>0.17300</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>84358402</td>\n      <td>M</td>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>135.10</td>\n      <td>1297.0</td>\n      <td>0.10030</td>\n      <td>0.13280</td>\n      <td>0.1980</td>\n      <td>0.10430</td>\n      <td>...</td>\n      <td>22.54</td>\n      <td>16.67</td>\n      <td>152.20</td>\n      <td>1575.0</td>\n      <td>0.1374</td>\n      <td>0.2050</td>\n      <td>0.4000</td>\n      <td>0.1625</td>\n      <td>0.2364</td>\n      <td>0.07678</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 32 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('bc_data.csv', header=0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "                 id  radius_mean  texture_mean  perimeter_mean    area_mean  \\\ncount  5.690000e+02   569.000000    569.000000      569.000000   569.000000   \nmean   3.037183e+07    14.127292     19.289649       91.969033   654.889104   \nstd    1.250206e+08     3.524049      4.301036       24.298981   351.914129   \nmin    8.670000e+03     6.981000      9.710000       43.790000   143.500000   \n25%    8.692180e+05    11.700000     16.170000       75.170000   420.300000   \n50%    9.060240e+05    13.370000     18.840000       86.240000   551.100000   \n75%    8.813129e+06    15.780000     21.800000      104.100000   782.700000   \nmax    9.113205e+08    28.110000     39.280000      188.500000  2501.000000   \n\n       smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\ncount       569.000000        569.000000      569.000000           569.000000   \nmean          0.096360          0.104341        0.088799             0.048919   \nstd           0.014064          0.052813        0.079720             0.038803   \nmin           0.052630          0.019380        0.000000             0.000000   \n25%           0.086370          0.064920        0.029560             0.020310   \n50%           0.095870          0.092630        0.061540             0.033500   \n75%           0.105300          0.130400        0.130700             0.074000   \nmax           0.163400          0.345400        0.426800             0.201200   \n\n       symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\ncount     569.000000  ...    569.000000     569.000000       569.000000   \nmean        0.181162  ...     16.269190      25.677223       107.261213   \nstd         0.027414  ...      4.833242       6.146258        33.602542   \nmin         0.106000  ...      7.930000      12.020000        50.410000   \n25%         0.161900  ...     13.010000      21.080000        84.110000   \n50%         0.179200  ...     14.970000      25.410000        97.660000   \n75%         0.195700  ...     18.790000      29.720000       125.400000   \nmax         0.304000  ...     36.040000      49.540000       251.200000   \n\n        area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\ncount   569.000000        569.000000         569.000000       569.000000   \nmean    880.583128          0.132369           0.254265         0.272188   \nstd     569.356993          0.022832           0.157336         0.208624   \nmin     185.200000          0.071170           0.027290         0.000000   \n25%     515.300000          0.116600           0.147200         0.114500   \n50%     686.500000          0.131300           0.211900         0.226700   \n75%    1084.000000          0.146000           0.339100         0.382900   \nmax    4254.000000          0.222600           1.058000         1.252000   \n\n       concave_points_worst  symmetry_worst  fractal_dimension_worst  \ncount            569.000000      569.000000               569.000000  \nmean               0.114606        0.290076                 0.083946  \nstd                0.065732        0.061867                 0.018061  \nmin                0.000000        0.156500                 0.055040  \n25%                0.064930        0.250400                 0.071460  \n50%                0.099930        0.282200                 0.080040  \n75%                0.161400        0.317900                 0.092080  \nmax                0.291000        0.663800                 0.207500  \n\n[8 rows x 31 columns]",
      "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>id</th>\n      <th>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>...</th>\n      <th>radius_worst</th>\n      <th>texture_worst</th>\n      <th>perimeter_worst</th>\n      <th>area_worst</th>\n      <th>smoothness_worst</th>\n      <th>compactness_worst</th>\n      <th>concavity_worst</th>\n      <th>concave_points_worst</th>\n      <th>symmetry_worst</th>\n      <th>fractal_dimension_worst</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5.690000e+02</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>...</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n      <td>569.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.037183e+07</td>\n      <td>14.127292</td>\n      <td>19.289649</td>\n      <td>91.969033</td>\n      <td>654.889104</td>\n      <td>0.096360</td>\n      <td>0.104341</td>\n      <td>0.088799</td>\n      <td>0.048919</td>\n      <td>0.181162</td>\n      <td>...</td>\n      <td>16.269190</td>\n      <td>25.677223</td>\n      <td>107.261213</td>\n      <td>880.583128</td>\n      <td>0.132369</td>\n      <td>0.254265</td>\n      <td>0.272188</td>\n      <td>0.114606</td>\n      <td>0.290076</td>\n      <td>0.083946</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.250206e+08</td>\n      <td>3.524049</td>\n      <td>4.301036</td>\n      <td>24.298981</td>\n      <td>351.914129</td>\n      <td>0.014064</td>\n      <td>0.052813</td>\n      <td>0.079720</td>\n      <td>0.038803</td>\n      <td>0.027414</td>\n      <td>...</td>\n      <td>4.833242</td>\n      <td>6.146258</td>\n      <td>33.602542</td>\n      <td>569.356993</td>\n      <td>0.022832</td>\n      <td>0.157336</td>\n      <td>0.208624</td>\n      <td>0.065732</td>\n      <td>0.061867</td>\n      <td>0.018061</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>8.670000e+03</td>\n      <td>6.981000</td>\n      <td>9.710000</td>\n      <td>43.790000</td>\n      <td>143.500000</td>\n      <td>0.052630</td>\n      <td>0.019380</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.106000</td>\n      <td>...</td>\n      <td>7.930000</td>\n      <td>12.020000</td>\n      <td>50.410000</td>\n      <td>185.200000</td>\n      <td>0.071170</td>\n      <td>0.027290</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.156500</td>\n      <td>0.055040</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>8.692180e+05</td>\n      <td>11.700000</td>\n      <td>16.170000</td>\n      <td>75.170000</td>\n      <td>420.300000</td>\n      <td>0.086370</td>\n      <td>0.064920</td>\n      <td>0.029560</td>\n      <td>0.020310</td>\n      <td>0.161900</td>\n      <td>...</td>\n      <td>13.010000</td>\n      <td>21.080000</td>\n      <td>84.110000</td>\n      <td>515.300000</td>\n      <td>0.116600</td>\n      <td>0.147200</td>\n      <td>0.114500</td>\n      <td>0.064930</td>\n      <td>0.250400</td>\n      <td>0.071460</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>9.060240e+05</td>\n      <td>13.370000</td>\n      <td>18.840000</td>\n      <td>86.240000</td>\n      <td>551.100000</td>\n      <td>0.095870</td>\n      <td>0.092630</td>\n      <td>0.061540</td>\n      <td>0.033500</td>\n      <td>0.179200</td>\n      <td>...</td>\n      <td>14.970000</td>\n      <td>25.410000</td>\n      <td>97.660000</td>\n      <td>686.500000</td>\n      <td>0.131300</td>\n      <td>0.211900</td>\n      <td>0.226700</td>\n      <td>0.099930</td>\n      <td>0.282200</td>\n      <td>0.080040</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>8.813129e+06</td>\n      <td>15.780000</td>\n      <td>21.800000</td>\n      <td>104.100000</td>\n      <td>782.700000</td>\n      <td>0.105300</td>\n      <td>0.130400</td>\n      <td>0.130700</td>\n      <td>0.074000</td>\n      <td>0.195700</td>\n      <td>...</td>\n      <td>18.790000</td>\n      <td>29.720000</td>\n      <td>125.400000</td>\n      <td>1084.000000</td>\n      <td>0.146000</td>\n      <td>0.339100</td>\n      <td>0.382900</td>\n      <td>0.161400</td>\n      <td>0.317900</td>\n      <td>0.092080</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>9.113205e+08</td>\n      <td>28.110000</td>\n      <td>39.280000</td>\n      <td>188.500000</td>\n      <td>2501.000000</td>\n      <td>0.163400</td>\n      <td>0.345400</td>\n      <td>0.426800</td>\n      <td>0.201200</td>\n      <td>0.304000</td>\n      <td>...</td>\n      <td>36.040000</td>\n      <td>49.540000</td>\n      <td>251.200000</td>\n      <td>4254.000000</td>\n      <td>0.222600</td>\n      <td>1.058000</td>\n      <td>1.252000</td>\n      <td>0.291000</td>\n      <td>0.663800</td>\n      <td>0.207500</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 31 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T07:40:42.053095Z",
     "start_time": "2024-02-29T07:40:42.023622Z"
    }
   },
   "id": "fde21927bab2d623",
   "execution_count": 5
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<bound method DataFrame.info of            id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n0      842302         M        17.99         10.38          122.80     1001.0   \n1      842517         M        20.57         17.77          132.90     1326.0   \n2    84300903         M        19.69         21.25          130.00     1203.0   \n3    84348301         M        11.42         20.38           77.58      386.1   \n4    84358402         M        20.29         14.34          135.10     1297.0   \n..        ...       ...          ...           ...             ...        ...   \n564    926424         M        21.56         22.39          142.00     1479.0   \n565    926682         M        20.13         28.25          131.20     1261.0   \n566    926954         M        16.60         28.08          108.30      858.1   \n567    927241         M        20.60         29.33          140.10     1265.0   \n568     92751         B         7.76         24.54           47.92      181.0   \n\n     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n0            0.11840           0.27760         0.30010              0.14710   \n1            0.08474           0.07864         0.08690              0.07017   \n2            0.10960           0.15990         0.19740              0.12790   \n3            0.14250           0.28390         0.24140              0.10520   \n4            0.10030           0.13280         0.19800              0.10430   \n..               ...               ...             ...                  ...   \n564          0.11100           0.11590         0.24390              0.13890   \n565          0.09780           0.10340         0.14400              0.09791   \n566          0.08455           0.10230         0.09251              0.05302   \n567          0.11780           0.27700         0.35140              0.15200   \n568          0.05263           0.04362         0.00000              0.00000   \n\n     ...  radius_worst  texture_worst  perimeter_worst  area_worst  \\\n0    ...        25.380          17.33           184.60      2019.0   \n1    ...        24.990          23.41           158.80      1956.0   \n2    ...        23.570          25.53           152.50      1709.0   \n3    ...        14.910          26.50            98.87       567.7   \n4    ...        22.540          16.67           152.20      1575.0   \n..   ...           ...            ...              ...         ...   \n564  ...        25.450          26.40           166.10      2027.0   \n565  ...        23.690          38.25           155.00      1731.0   \n566  ...        18.980          34.12           126.70      1124.0   \n567  ...        25.740          39.42           184.60      1821.0   \n568  ...         9.456          30.37            59.16       268.6   \n\n     smoothness_worst  compactness_worst  concavity_worst  \\\n0             0.16220            0.66560           0.7119   \n1             0.12380            0.18660           0.2416   \n2             0.14440            0.42450           0.4504   \n3             0.20980            0.86630           0.6869   \n4             0.13740            0.20500           0.4000   \n..                ...                ...              ...   \n564           0.14100            0.21130           0.4107   \n565           0.11660            0.19220           0.3215   \n566           0.11390            0.30940           0.3403   \n567           0.16500            0.86810           0.9387   \n568           0.08996            0.06444           0.0000   \n\n     concave_points_worst  symmetry_worst  fractal_dimension_worst  \n0                  0.2654          0.4601                  0.11890  \n1                  0.1860          0.2750                  0.08902  \n2                  0.2430          0.3613                  0.08758  \n3                  0.2575          0.6638                  0.17300  \n4                  0.1625          0.2364                  0.07678  \n..                    ...             ...                      ...  \n564                0.2216          0.2060                  0.07115  \n565                0.1628          0.2572                  0.06637  \n566                0.1418          0.2218                  0.07820  \n567                0.2650          0.4087                  0.12400  \n568                0.0000          0.2871                  0.07039  \n\n[569 rows x 32 columns]>"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T07:40:50.658398Z",
     "start_time": "2024-02-29T07:40:50.649836Z"
    }
   },
   "id": "33a333275b0acb62",
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "(569, 32)"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T07:40:53.273389Z",
     "start_time": "2024-02-29T07:40:53.269960Z"
    }
   },
   "id": "cf2a7dc50f963f7a",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "   radius_mean  texture_mean  perimeter_mean  area_mean  smoothness_mean  \\\n0        17.99         10.38          122.80     1001.0          0.11840   \n1        20.57         17.77          132.90     1326.0          0.08474   \n2        19.69         21.25          130.00     1203.0          0.10960   \n3        11.42         20.38           77.58      386.1          0.14250   \n4        20.29         14.34          135.10     1297.0          0.10030   \n\n   compactness_mean  concavity_mean  concave points_mean  symmetry_mean  \\\n0           0.27760          0.3001              0.14710         0.2419   \n1           0.07864          0.0869              0.07017         0.1812   \n2           0.15990          0.1974              0.12790         0.2069   \n3           0.28390          0.2414              0.10520         0.2597   \n4           0.13280          0.1980              0.10430         0.1809   \n\n   fractal_dimension_mean  \n0                 0.07871  \n1                 0.05667  \n2                 0.05999  \n3                 0.09744  \n4                 0.05883  ",
      "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>radius_mean</th>\n      <th>texture_mean</th>\n      <th>perimeter_mean</th>\n      <th>area_mean</th>\n      <th>smoothness_mean</th>\n      <th>compactness_mean</th>\n      <th>concavity_mean</th>\n      <th>concave points_mean</th>\n      <th>symmetry_mean</th>\n      <th>fractal_dimension_mean</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17.99</td>\n      <td>10.38</td>\n      <td>122.80</td>\n      <td>1001.0</td>\n      <td>0.11840</td>\n      <td>0.27760</td>\n      <td>0.3001</td>\n      <td>0.14710</td>\n      <td>0.2419</td>\n      <td>0.07871</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>20.57</td>\n      <td>17.77</td>\n      <td>132.90</td>\n      <td>1326.0</td>\n      <td>0.08474</td>\n      <td>0.07864</td>\n      <td>0.0869</td>\n      <td>0.07017</td>\n      <td>0.1812</td>\n      <td>0.05667</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>19.69</td>\n      <td>21.25</td>\n      <td>130.00</td>\n      <td>1203.0</td>\n      <td>0.10960</td>\n      <td>0.15990</td>\n      <td>0.1974</td>\n      <td>0.12790</td>\n      <td>0.2069</td>\n      <td>0.05999</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>11.42</td>\n      <td>20.38</td>\n      <td>77.58</td>\n      <td>386.1</td>\n      <td>0.14250</td>\n      <td>0.28390</td>\n      <td>0.2414</td>\n      <td>0.10520</td>\n      <td>0.2597</td>\n      <td>0.09744</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>20.29</td>\n      <td>14.34</td>\n      <td>135.10</td>\n      <td>1297.0</td>\n      <td>0.10030</td>\n      <td>0.13280</td>\n      <td>0.1980</td>\n      <td>0.10430</td>\n      <td>0.1809</td>\n      <td>0.05883</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = df.iloc[:, 2:12]\n",
    "features.head()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T08:59:29.955013Z",
     "start_time": "2024-02-29T08:59:29.946763Z"
    }
   },
   "id": "de2f104bfe69a01",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M' 'M']\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "diagnosis = df[\"diagnosis\"]\n",
    "diagnosis_array = np.ravel(diagnosis)\n",
    "print(diagnosis_array[:10])"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:01:30.707342Z",
     "start_time": "2024-02-29T09:01:30.703571Z"
    }
   },
   "id": "2d9f42ce7a423f09",
   "execution_count": 11
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集大小: (426, 10)\n",
      "测试集大小: (143, 10)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(features, diagnosis_array, test_size=0.25, random_state=42)\n",
    "print(\"训练集大小:\", X_train.shape)\n",
    "print(\"测试集大小:\", X_test.shape)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:01:51.137834Z",
     "start_time": "2024-02-29T09:01:50.972999Z"
    }
   },
   "id": "3b7128a6f067dbb5",
   "execution_count": 12
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "KNeighborsClassifier()",
      "text/html": "<style>#sk-container-id-2 {\n  /* Definition of color scheme common for light and dark mode */\n  --sklearn-color-text: black;\n  --sklearn-color-line: gray;\n  /* Definition of color scheme for unfitted estimators */\n  --sklearn-color-unfitted-level-0: #fff5e6;\n  --sklearn-color-unfitted-level-1: #f6e4d2;\n  --sklearn-color-unfitted-level-2: #ffe0b3;\n  --sklearn-color-unfitted-level-3: chocolate;\n  /* Definition of color scheme for fitted estimators */\n  --sklearn-color-fitted-level-0: #f0f8ff;\n  --sklearn-color-fitted-level-1: #d4ebff;\n  --sklearn-color-fitted-level-2: #b3dbfd;\n  --sklearn-color-fitted-level-3: cornflowerblue;\n\n  /* Specific color for light theme */\n  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n  --sklearn-color-icon: #696969;\n\n  @media (prefers-color-scheme: dark) {\n    /* Redefinition of color scheme for dark theme */\n    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n    --sklearn-color-icon: #878787;\n  }\n}\n\n#sk-container-id-2 {\n  color: var(--sklearn-color-text);\n}\n\n#sk-container-id-2 pre {\n  padding: 0;\n}\n\n#sk-container-id-2 input.sk-hidden--visually {\n  border: 0;\n  clip: rect(1px 1px 1px 1px);\n  clip: rect(1px, 1px, 1px, 1px);\n  height: 1px;\n  margin: -1px;\n  overflow: hidden;\n  padding: 0;\n  position: absolute;\n  width: 1px;\n}\n\n#sk-container-id-2 div.sk-dashed-wrapped {\n  border: 1px dashed var(--sklearn-color-line);\n  margin: 0 0.4em 0.5em 0.4em;\n  box-sizing: border-box;\n  padding-bottom: 0.4em;\n  background-color: var(--sklearn-color-background);\n}\n\n#sk-container-id-2 div.sk-container {\n  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n     but bootstrap.min.css set `[hidden] { display: none !important; }`\n     so we also need the `!important` here to be able to override the\n     default hidden behavior on the sphinx rendered scikit-learn.org.\n     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n  display: inline-block !important;\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-text-repr-fallback {\n  display: none;\n}\n\ndiv.sk-parallel-item,\ndiv.sk-serial,\ndiv.sk-item {\n  /* draw centered vertical line to link estimators */\n  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n  background-size: 2px 100%;\n  background-repeat: no-repeat;\n  background-position: center center;\n}\n\n/* Parallel-specific style estimator block */\n\n#sk-container-id-2 div.sk-parallel-item::after {\n  content: \"\";\n  width: 100%;\n  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n  flex-grow: 1;\n}\n\n#sk-container-id-2 div.sk-parallel {\n  display: flex;\n  align-items: stretch;\n  justify-content: center;\n  background-color: var(--sklearn-color-background);\n  position: relative;\n}\n\n#sk-container-id-2 div.sk-parallel-item {\n  display: flex;\n  flex-direction: column;\n}\n\n#sk-container-id-2 div.sk-parallel-item:first-child::after {\n  align-self: flex-end;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:last-child::after {\n  align-self: flex-start;\n  width: 50%;\n}\n\n#sk-container-id-2 div.sk-parallel-item:only-child::after {\n  width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-2 div.sk-serial {\n  display: flex;\n  flex-direction: column;\n  align-items: center;\n  background-color: var(--sklearn-color-background);\n  padding-right: 1em;\n  padding-left: 1em;\n}\n\n\n/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\nclickable and can be expanded/collapsed.\n- Pipeline and ColumnTransformer use this feature and define the default style\n- Estimators will overwrite some part of the style using the `sk-estimator` class\n*/\n\n/* Pipeline and ColumnTransformer style (default) */\n\n#sk-container-id-2 div.sk-toggleable {\n  /* Default theme specific background. It is overwritten whether we have a\n  specific estimator or a Pipeline/ColumnTransformer */\n  background-color: var(--sklearn-color-background);\n}\n\n/* Toggleable label */\n#sk-container-id-2 label.sk-toggleable__label {\n  cursor: pointer;\n  display: block;\n  width: 100%;\n  margin-bottom: 0;\n  padding: 0.5em;\n  box-sizing: border-box;\n  text-align: center;\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n  /* Arrow on the left of the label */\n  content: \"▸\";\n  float: left;\n  margin-right: 0.25em;\n  color: var(--sklearn-color-icon);\n}\n\n#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n  color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-2 div.sk-toggleable__content {\n  max-height: 0;\n  max-width: 0;\n  overflow: hidden;\n  text-align: left;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content pre {\n  margin: 0.2em;\n  border-radius: 0.25em;\n  color: var(--sklearn-color-text);\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n  /* unfitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n  /* Expand drop-down */\n  max-height: 200px;\n  max-width: 100%;\n  overflow: auto;\n}\n\n#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n  content: \"▾\";\n}\n\n/* Pipeline/ColumnTransformer-specific style */\n\n#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator-specific style */\n\n/* Colorize estimator box */\n#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n#sk-container-id-2 div.sk-label label {\n  /* The background is the default theme color */\n  color: var(--sklearn-color-text-on-default-background);\n}\n\n/* On hover, darken the color of the background */\n#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n/* Label box, darken color on hover, fitted */\n#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n  color: var(--sklearn-color-text);\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Estimator label */\n\n#sk-container-id-2 div.sk-label label {\n  font-family: monospace;\n  font-weight: bold;\n  display: inline-block;\n  line-height: 1.2em;\n}\n\n#sk-container-id-2 div.sk-label-container {\n  text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-2 div.sk-estimator {\n  font-family: monospace;\n  border: 1px dotted var(--sklearn-color-border-box);\n  border-radius: 0.25em;\n  box-sizing: border-box;\n  margin-bottom: 0.5em;\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-0);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-2 div.sk-estimator:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-2 div.sk-estimator.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-2);\n}\n\n/* Specification for estimator info (e.g. \"i\" and \"?\") */\n\n/* Common style for \"i\" and \"?\" */\n\n.sk-estimator-doc-link,\na:link.sk-estimator-doc-link,\na:visited.sk-estimator-doc-link {\n  float: right;\n  font-size: smaller;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1em;\n  height: 1em;\n  width: 1em;\n  text-decoration: none !important;\n  margin-left: 1ex;\n  /* unfitted */\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n  color: var(--sklearn-color-unfitted-level-1);\n}\n\n.sk-estimator-doc-link.fitted,\na:link.sk-estimator-doc-link.fitted,\na:visited.sk-estimator-doc-link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\ndiv.sk-estimator:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link:hover,\n.sk-estimator-doc-link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\ndiv.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover,\ndiv.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n.sk-estimator-doc-link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n/* Span, style for the box shown on hovering the info icon */\n.sk-estimator-doc-link span {\n  display: none;\n  z-index: 9999;\n  position: relative;\n  font-weight: normal;\n  right: .2ex;\n  padding: .5ex;\n  margin: .5ex;\n  width: min-content;\n  min-width: 20ex;\n  max-width: 50ex;\n  color: var(--sklearn-color-text);\n  box-shadow: 2pt 2pt 4pt #999;\n  /* unfitted */\n  background: var(--sklearn-color-unfitted-level-0);\n  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n}\n\n.sk-estimator-doc-link.fitted span {\n  /* fitted */\n  background: var(--sklearn-color-fitted-level-0);\n  border: var(--sklearn-color-fitted-level-3);\n}\n\n.sk-estimator-doc-link:hover span {\n  display: block;\n}\n\n/* \"?\"-specific style due to the `<a>` HTML tag */\n\n#sk-container-id-2 a.estimator_doc_link {\n  float: right;\n  font-size: 1rem;\n  line-height: 1em;\n  font-family: monospace;\n  background-color: var(--sklearn-color-background);\n  border-radius: 1rem;\n  height: 1rem;\n  width: 1rem;\n  text-decoration: none;\n  /* unfitted */\n  color: var(--sklearn-color-unfitted-level-1);\n  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted {\n  /* fitted */\n  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n  color: var(--sklearn-color-fitted-level-1);\n}\n\n/* On hover */\n#sk-container-id-2 a.estimator_doc_link:hover {\n  /* unfitted */\n  background-color: var(--sklearn-color-unfitted-level-3);\n  color: var(--sklearn-color-background);\n  text-decoration: none;\n}\n\n#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n  /* fitted */\n  background-color: var(--sklearn-color-fitted-level-3);\n}\n</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn = KNeighborsClassifier()\n",
    "knn.fit(X_train, y_train)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:04:21.655762Z",
     "start_time": "2024-02-29T09:04:21.649291Z"
    }
   },
   "id": "b2e04debfef26d18",
   "execution_count": 14
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集预测结果： ['B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B'\n",
      " 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M'\n",
      " 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'B'\n",
      " 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'B'\n",
      " 'M' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M'\n",
      " 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'M'\n",
      " 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'B'\n",
      " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M'\n",
      " 'M' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'M' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M' 'M' 'B'\n",
      " 'B' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M'\n",
      " 'B' 'M' 'M' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B'\n",
      " 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'M' 'M' 'M' 'M' 'B' 'B' 'B' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'B'\n",
      " 'M' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'M' 'M' 'B' 'B' 'M' 'M' 'M' 'B'\n",
      " 'B' 'M' 'M' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'B' 'B'\n",
      " 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B' 'M' 'M'\n",
      " 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B']\n",
      "测试集预测结果： ['B' 'M' 'M' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B' 'M' 'B' 'B'\n",
      " 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B'\n",
      " 'M' 'B' 'M' 'B' 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'M' 'B' 'B'\n",
      " 'B' 'B' 'B' 'M' 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M'\n",
      " 'B' 'M' 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'M' 'M' 'M' 'M' 'B' 'M' 'B' 'B'\n",
      " 'B' 'B' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'M' 'B' 'M' 'M' 'B' 'B' 'B' 'M'\n",
      " 'B' 'B' 'M' 'B' 'M' 'M' 'B' 'M' 'B' 'B' 'B' 'B' 'M' 'B' 'B' 'M' 'B' 'M'\n",
      " 'B' 'B' 'B' 'M' 'M' 'M' 'B' 'B' 'M' 'M' 'B' 'B' 'B' 'M' 'B' 'M' 'B']\n"
     ]
    }
   ],
   "source": [
    "y_pred_train = knn.predict(X_train)\n",
    "y_pred_test = knn.predict(X_test)\n",
    "print(\"训练集预测结果：\", y_pred_train)\n",
    "print(\"测试集预测结果：\", y_pred_test)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:05:17.922504Z",
     "start_time": "2024-02-29T09:05:17.904693Z"
    }
   },
   "id": "cac5ad8ea65fbab3",
   "execution_count": 15
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'algorithm': 'auto', 'leaf_size': 30, 'metric': 'minkowski', 'metric_params': None, 'n_jobs': None, 'n_neighbors': 5, 'p': 2, 'weights': 'uniform'}\n"
     ]
    }
   ],
   "source": [
    "print(knn.get_params())"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:05:48.533747Z",
     "start_time": "2024-02-29T09:05:48.530345Z"
    }
   },
   "id": "34874ff1a197b193",
   "execution_count": 16
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集准确率： 0.8967136150234741\n",
      "测试集准确率： 0.916083916083916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "train_accuracy = accuracy_score(y_train, y_pred_train)\n",
    "test_accuracy = accuracy_score(y_test, y_pred_test)\n",
    "print(\"训练集准确率：\", train_accuracy)\n",
    "print(\"测试集准确率：\", test_accuracy)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:06:39.831762Z",
     "start_time": "2024-02-29T09:06:39.827707Z"
    }
   },
   "id": "a3ffa5d01d23080b",
   "execution_count": 17
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pylab import mpl\n",
    "\n",
    "mpl.rcParams[\"font.sans-serif\"] = [\"SimHei\"]\n",
    "\n",
    "k_values = range(1, 23)\n",
    "accuracies = []\n",
    "for k in k_values:\n",
    "    knn = KNeighborsClassifier(n_neighbors=k)\n",
    "    knn.fit(X_train, y_train)\n",
    "    pred = knn.predict(X_test)\n",
    "    accuracies.append(accuracy_score(y_test, pred))\n",
    "plt.plot(k_values, accuracies)\n",
    "plt.xlabel('k值')\n",
    "plt.ylabel('准确率')\n",
    "plt.title('不同k值下的准确率')\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-02-29T09:20:26.178865Z",
     "start_time": "2024-02-29T09:20:25.994659Z"
    }
   },
   "id": "42ff8fb694530848",
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "d96ff8b92c302f60"
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}