415 lines
50 KiB
Plaintext
415 lines
50 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "initial_id",
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"metadata": {
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||
"collapsed": true,
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||
"ExecuteTime": {
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"end_time": "2024-03-03T12:37:14.219767Z",
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"start_time": "2024-03-03T12:37:14.199729Z"
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" id diagnosis radius_mean texture_mean perimeter_mean area_mean \\\n",
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"0 842302 M 17.99 10.38 122.80 1001.0 \n",
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"1 842517 M 20.57 17.77 132.90 1326.0 \n",
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"2 84300903 M 19.69 21.25 130.00 1203.0 \n",
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"3 84348301 M 11.42 20.38 77.58 386.1 \n",
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"4 84358402 M 20.29 14.34 135.10 1297.0 \n",
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"\n",
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" smoothness_mean compactness_mean concavity_mean concave points_mean \\\n",
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"0 0.11840 0.27760 0.3001 0.14710 \n",
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"1 0.08474 0.07864 0.0869 0.07017 \n",
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"2 0.10960 0.15990 0.1974 0.12790 \n",
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"3 0.14250 0.28390 0.2414 0.10520 \n",
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"4 0.10030 0.13280 0.1980 0.10430 \n",
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"\n",
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" ... radius_worst texture_worst perimeter_worst area_worst \\\n",
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"0 ... 25.38 17.33 184.60 2019.0 \n",
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"1 ... 24.99 23.41 158.80 1956.0 \n",
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"2 ... 23.57 25.53 152.50 1709.0 \n",
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"3 ... 14.91 26.50 98.87 567.7 \n",
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"4 ... 22.54 16.67 152.20 1575.0 \n",
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"\n",
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" smoothness_worst compactness_worst concavity_worst concave_points_worst \\\n",
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"0 0.1622 0.6656 0.7119 0.2654 \n",
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"1 0.1238 0.1866 0.2416 0.1860 \n",
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"2 0.1444 0.4245 0.4504 0.2430 \n",
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"3 0.2098 0.8663 0.6869 0.2575 \n",
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"4 0.1374 0.2050 0.4000 0.1625 \n",
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"\n",
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" symmetry_worst fractal_dimension_worst \n",
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"0 0.4601 0.11890 \n",
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"1 0.2750 0.08902 \n",
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"2 0.3613 0.08758 \n",
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"3 0.6638 0.17300 \n",
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"4 0.2364 0.07678 \n",
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"\n",
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"[5 rows x 32 columns]\n"
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]
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}
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],
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"source": [
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"import pandas as pd\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.naive_bayes import GaussianNB\n",
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"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
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"\n",
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"data = pd.read_csv(\"bc_data.csv\")\n",
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"print(data.head())"
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]
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"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]",
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"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>"
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},
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"execution_count": 4,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.describe()\n"
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],
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"metadata": {
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||
"collapsed": false,
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||
"ExecuteTime": {
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||
"end_time": "2024-03-03T12:37:34.624072Z",
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"start_time": "2024-03-03T12:37:34.592026Z"
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}
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},
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"id": "ce5939bbd4b11119",
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"execution_count": 4
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"<class 'pandas.core.frame.DataFrame'>\n",
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"RangeIndex: 569 entries, 0 to 568\n",
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"Data columns (total 32 columns):\n",
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" # Column Non-Null Count Dtype \n",
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"--- ------ -------------- ----- \n",
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" 0 id 569 non-null int64 \n",
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" 1 diagnosis 569 non-null object \n",
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" 2 radius_mean 569 non-null float64\n",
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" 3 texture_mean 569 non-null float64\n",
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" 4 perimeter_mean 569 non-null float64\n",
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" 5 area_mean 569 non-null float64\n",
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" 6 smoothness_mean 569 non-null float64\n",
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" 7 compactness_mean 569 non-null float64\n",
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" 8 concavity_mean 569 non-null float64\n",
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" 9 concave points_mean 569 non-null float64\n",
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" 10 symmetry_mean 569 non-null float64\n",
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" 11 fractal_dimension_mean 569 non-null float64\n",
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" 12 radius_se 569 non-null float64\n",
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" 13 texture_se 569 non-null float64\n",
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" 14 perimeter_se 569 non-null float64\n",
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" 15 area_se 569 non-null float64\n",
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" 16 smoothness_se 569 non-null float64\n",
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" 17 compactness_se 569 non-null float64\n",
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" 18 concavity_se 569 non-null float64\n",
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" 19 concave points_se 569 non-null float64\n",
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" 20 symmetry_se 569 non-null float64\n",
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" 21 fractal_dimension_se 569 non-null float64\n",
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" 22 radius_worst 569 non-null float64\n",
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" 23 texture_worst 569 non-null float64\n",
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" 24 perimeter_worst 569 non-null float64\n",
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" 25 area_worst 569 non-null float64\n",
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" 26 smoothness_worst 569 non-null float64\n",
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" 27 compactness_worst 569 non-null float64\n",
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" 28 concavity_worst 569 non-null float64\n",
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" 29 concave_points_worst 569 non-null float64\n",
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" 30 symmetry_worst 569 non-null float64\n",
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" 31 fractal_dimension_worst 569 non-null float64\n",
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"dtypes: float64(30), int64(1), object(1)\n",
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"memory usage: 142.4+ KB\n"
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]
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}
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],
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"source": [
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"data.info()\n"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T12:37:39.116253Z",
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"start_time": "2024-03-03T12:37:39.110965Z"
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}
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},
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"id": "968d59778944a1be",
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"execution_count": 5
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": "(569, 32)"
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.shape"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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"end_time": "2024-03-03T12:37:40.558748Z",
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||
"start_time": "2024-03-03T12:37:40.555663Z"
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}
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},
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"id": "8a804c27a82f52c0",
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"execution_count": 6
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},
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{
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"cell_type": "code",
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"outputs": [
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{
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"data": {
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"text/plain": "id 0\ndiagnosis 0\nradius_mean 0\ntexture_mean 0\nperimeter_mean 0\narea_mean 0\nsmoothness_mean 0\ncompactness_mean 0\nconcavity_mean 0\nconcave points_mean 0\nsymmetry_mean 0\nfractal_dimension_mean 0\nradius_se 0\ntexture_se 0\nperimeter_se 0\narea_se 0\nsmoothness_se 0\ncompactness_se 0\nconcavity_se 0\nconcave points_se 0\nsymmetry_se 0\nfractal_dimension_se 0\nradius_worst 0\ntexture_worst 0\nperimeter_worst 0\narea_worst 0\nsmoothness_worst 0\ncompactness_worst 0\nconcavity_worst 0\nconcave_points_worst 0\nsymmetry_worst 0\nfractal_dimension_worst 0\ndtype: int64"
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"data.isnull().sum()\n"
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],
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"metadata": {
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||
"collapsed": false,
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||
"ExecuteTime": {
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||
"end_time": "2024-03-03T12:38:05.672870Z",
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||
"start_time": "2024-03-03T12:38:05.666989Z"
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||
}
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},
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"id": "27fb5c5ebe568c89",
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"execution_count": 7
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"from sklearn.preprocessing import LabelEncoder\n",
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"\n",
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"le = LabelEncoder()\n",
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"data[\"diagnosis\"] = le.fit_transform(data[\"diagnosis\"])"
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],
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"metadata": {
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"collapsed": false,
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"ExecuteTime": {
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||
"end_time": "2024-03-03T12:38:14.612210Z",
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||
"start_time": "2024-03-03T12:38:14.608859Z"
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}
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},
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"id": "921f15387d3f20d",
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"execution_count": 8
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},
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{
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"cell_type": "code",
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(data.drop(\"diagnosis\", axis=1), \n",
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" data[\"diagnosis\"], \n",
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" test_size=0.25, \n",
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" random_state=0)"
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],
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"metadata": {
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||
"collapsed": false,
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||
"ExecuteTime": {
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||
"end_time": "2024-03-03T12:38:22.649922Z",
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||
"start_time": "2024-03-03T12:38:22.645136Z"
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}
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},
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"id": "65fb42d7ccf169e3",
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"execution_count": 9
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},
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{
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"cell_type": "code",
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"outputs": [
|
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"训练集数据量： (426, 31)\n",
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"测试集数据量： (143, 31)\n"
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]
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}
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],
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"source": [
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"print(\"训练集数据量：\", X_train.shape)\n",
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"print(\"测试集数据量：\", X_test.shape)\n"
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],
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"metadata": {
|
||
"collapsed": false,
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||
"ExecuteTime": {
|
||
"end_time": "2024-03-03T12:38:40.264544Z",
|
||
"start_time": "2024-03-03T12:38:40.261072Z"
|
||
}
|
||
},
|
||
"id": "6d82d79ea7a3609f",
|
||
"execution_count": 10
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "GaussianNB()",
|
||
"text/html": "<style>#sk-container-id-1 {\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-1 {\n color: var(--sklearn-color-text);\n}\n\n#sk-container-id-1 pre {\n padding: 0;\n}\n\n#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-parallel-item {\n display: flex;\n flex-direction: column;\n}\n\n#sk-container-id-1 div.sk-parallel-item:first-child::after {\n align-self: flex-end;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:last-child::after {\n align-self: flex-start;\n width: 50%;\n}\n\n#sk-container-id-1 div.sk-parallel-item:only-child::after {\n width: 0;\n}\n\n/* Serial-specific style estimator block */\n\n#sk-container-id-1 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-1 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-1 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-1 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-1 label.sk-toggleable__label-arrow:hover:before {\n color: var(--sklearn-color-text);\n}\n\n/* Toggleable content - dropdown */\n\n#sk-container-id-1 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-1 div.sk-toggleable__content.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 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-1 div.sk-toggleable__content.fitted pre {\n /* unfitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n#sk-container-id-1 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-1 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-1 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-1 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-1 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-1 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-1 div.sk-label label.sk-toggleable__label,\n#sk-container-id-1 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-1 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-1 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-1 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-1 div.sk-label-container {\n text-align: center;\n}\n\n/* Estimator-specific */\n#sk-container-id-1 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-1 div.sk-estimator.fitted {\n /* fitted */\n background-color: var(--sklearn-color-fitted-level-0);\n}\n\n/* on hover */\n#sk-container-id-1 div.sk-estimator:hover {\n /* unfitted */\n background-color: var(--sklearn-color-unfitted-level-2);\n}\n\n#sk-container-id-1 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-1 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-1 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-1 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-1 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-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div>"
|
||
},
|
||
"execution_count": 11,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"model = GaussianNB()\n",
|
||
"model.fit(X_train, y_train)\n"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-03T12:38:47.313263Z",
|
||
"start_time": "2024-03-03T12:38:47.307431Z"
|
||
}
|
||
},
|
||
"id": "15f256db08ba4d28",
|
||
"execution_count": 11
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"准确率： 0.6363636363636364\n",
|
||
"精确率： 0.6666666666666666\n",
|
||
"召回率： 0.03773584905660377\n",
|
||
"F1 值： 0.07142857142857142\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"y_pred = model.predict(X_test)\n",
|
||
"print(\"准确率：\", accuracy_score(y_test, y_pred))\n",
|
||
"print(\"精确率：\", precision_score(y_test, y_pred))\n",
|
||
"print(\"召回率：\", recall_score(y_test, y_pred))\n",
|
||
"print(\"F1 值：\", f1_score(y_test, y_pred))\n"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-03T12:40:07.656413Z",
|
||
"start_time": "2024-03-03T12:40:07.647268Z"
|
||
}
|
||
},
|
||
"id": "85299c4605550166",
|
||
"execution_count": 13
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"text/plain": "GridSearchCV(cv=5, estimator=GaussianNB(),\n param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11,\n 1e-12]},\n scoring='accuracy')",
|
||
"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>GridSearchCV(cv=5, estimator=GaussianNB(),\n param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11,\n 1e-12]},\n scoring='accuracy')</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 sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=5, estimator=GaussianNB(),\n param_grid={'var_smoothing': [1e-07, 1e-08, 1e-09, 1e-10, 1e-11,\n 1e-12]},\n scoring='accuracy')</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: GaussianNB</label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\"> GaussianNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.GaussianNB.html\">?<span>Documentation for GaussianNB</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>GaussianNB()</pre></div> </div></div></div></div></div></div></div></div></div>"
|
||
},
|
||
"execution_count": 14,
|
||
"metadata": {},
|
||
"output_type": "execute_result"
|
||
}
|
||
],
|
||
"source": [
|
||
"from sklearn.model_selection import GridSearchCV\n",
|
||
"\n",
|
||
"param_grid = {\"var_smoothing\": [1e-7, 1e-8, 1e-9, 1e-10, 1e-11, 1e-12]}\n",
|
||
"\n",
|
||
"grid_search = GridSearchCV(GaussianNB(), param_grid=param_grid, scoring=\"accuracy\", cv=5)\n",
|
||
"grid_search.fit(X_train, y_train)\n"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-03T12:40:16.442517Z",
|
||
"start_time": "2024-03-03T12:40:16.378989Z"
|
||
}
|
||
},
|
||
"id": "5076117fd493d87b",
|
||
"execution_count": 14
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"outputs": [
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"最优参数： {'var_smoothing': 1e-12}\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"print(\"最优参数：\", grid_search.best_params_)\n"
|
||
],
|
||
"metadata": {
|
||
"collapsed": false,
|
||
"ExecuteTime": {
|
||
"end_time": "2024-03-03T12:40:59.139337Z",
|
||
"start_time": "2024-03-03T12:40:59.136232Z"
|
||
}
|
||
},
|
||
"id": "5671b41a8291920",
|
||
"execution_count": 15
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"outputs": [],
|
||
"source": [],
|
||
"metadata": {
|
||
"collapsed": false
|
||
},
|
||
"id": "5eb3263bc83c44da"
|
||
}
|
||
],
|
||
"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
|
||
}
|