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Load Brest Cancer-SVMs.ipynb
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load-brest-cancer-svms.ipynb
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"<a href=\"https://colab.research.google.com/gist/yamil-abraham/95ce7cdd3968ada1f38daa77f186d4e9/load-brest-cancer-svms.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "X3RRfOu95QaT"
},
"outputs": [],
"source": [
"from sklearn import datasets\n",
"\n",
"data = datasets.load_breast_cancer(as_frame=True)\n",
"#https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_breast_cancer.html"
]
},
{
"cell_type": "code",
"source": [
"data.data"
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{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
".. ... ... ... ... ... \n",
"564 21.56 22.39 142.00 1479.0 0.11100 \n",
"565 20.13 28.25 131.20 1261.0 0.09780 \n",
"566 16.60 28.08 108.30 858.1 0.08455 \n",
"567 20.60 29.33 140.10 1265.0 0.11780 \n",
"568 7.76 24.54 47.92 181.0 0.05263 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.30010 0.14710 0.2419 \n",
"1 0.07864 0.08690 0.07017 0.1812 \n",
"2 0.15990 0.19740 0.12790 0.2069 \n",
"3 0.28390 0.24140 0.10520 0.2597 \n",
"4 0.13280 0.19800 0.10430 0.1809 \n",
".. ... ... ... ... \n",
"564 0.11590 0.24390 0.13890 0.1726 \n",
"565 0.10340 0.14400 0.09791 0.1752 \n",
"566 0.10230 0.09251 0.05302 0.1590 \n",
"567 0.27700 0.35140 0.15200 0.2397 \n",
"568 0.04362 0.00000 0.00000 0.1587 \n",
"\n",
" mean fractal dimension ... worst radius worst texture \\\n",
"0 0.07871 ... 25.380 17.33 \n",
"1 0.05667 ... 24.990 23.41 \n",
"2 0.05999 ... 23.570 25.53 \n",
"3 0.09744 ... 14.910 26.50 \n",
"4 0.05883 ... 22.540 16.67 \n",
".. ... ... ... ... \n",
"564 0.05623 ... 25.450 26.40 \n",
"565 0.05533 ... 23.690 38.25 \n",
"566 0.05648 ... 18.980 34.12 \n",
"567 0.07016 ... 25.740 39.42 \n",
"568 0.05884 ... 9.456 30.37 \n",
"\n",
" worst perimeter worst area worst smoothness worst compactness \\\n",
"0 184.60 2019.0 0.16220 0.66560 \n",
"1 158.80 1956.0 0.12380 0.18660 \n",
"2 152.50 1709.0 0.14440 0.42450 \n",
"3 98.87 567.7 0.20980 0.86630 \n",
"4 152.20 1575.0 0.13740 0.20500 \n",
".. ... ... ... ... \n",
"564 166.10 2027.0 0.14100 0.21130 \n",
"565 155.00 1731.0 0.11660 0.19220 \n",
"566 126.70 1124.0 0.11390 0.30940 \n",
"567 184.60 1821.0 0.16500 0.86810 \n",
"568 59.16 268.6 0.08996 0.06444 \n",
"\n",
" worst concavity worst concave points worst symmetry \\\n",
"0 0.7119 0.2654 0.4601 \n",
"1 0.2416 0.1860 0.2750 \n",
"2 0.4504 0.2430 0.3613 \n",
"3 0.6869 0.2575 0.6638 \n",
"4 0.4000 0.1625 0.2364 \n",
".. ... ... ... \n",
"564 0.4107 0.2216 0.2060 \n",
"565 0.3215 0.1628 0.2572 \n",
"566 0.3403 0.1418 0.2218 \n",
"567 0.9387 0.2650 0.4087 \n",
"568 0.0000 0.0000 0.2871 \n",
"\n",
" worst fractal dimension \n",
"0 0.11890 \n",
"1 0.08902 \n",
"2 0.08758 \n",
"3 0.17300 \n",
"4 0.07678 \n",
".. ... \n",
"564 0.07115 \n",
"565 0.06637 \n",
"566 0.07820 \n",
"567 0.12400 \n",
"568 0.07039 \n",
"\n",
"[569 rows x 30 columns]"
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
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" <th>...</th>\n",
" <th>worst radius</th>\n",
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" <th>worst compactness</th>\n",
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" <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",
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" <td>0.14710</td>\n",
" <td>0.2419</td>\n",
" <td>0.07871</td>\n",
" <td>...</td>\n",
" <td>25.380</td>\n",
" <td>17.33</td>\n",
" <td>184.60</td>\n",
" <td>2019.0</td>\n",
" <td>0.16220</td>\n",
" <td>0.66560</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>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>0.08690</td>\n",
" <td>0.07017</td>\n",
" <td>0.1812</td>\n",
" <td>0.05667</td>\n",
" <td>...</td>\n",
" <td>24.990</td>\n",
" <td>23.41</td>\n",
" <td>158.80</td>\n",
" <td>1956.0</td>\n",
" <td>0.12380</td>\n",
" <td>0.18660</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>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>0.19740</td>\n",
" <td>0.12790</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
" <td>...</td>\n",
" <td>23.570</td>\n",
" <td>25.53</td>\n",
" <td>152.50</td>\n",
" <td>1709.0</td>\n",
" <td>0.14440</td>\n",
" <td>0.42450</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>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>0.24140</td>\n",
" <td>0.10520</td>\n",
" <td>0.2597</td>\n",
" <td>0.09744</td>\n",
" <td>...</td>\n",
" <td>14.910</td>\n",
" <td>26.50</td>\n",
" <td>98.87</td>\n",
" <td>567.7</td>\n",
" <td>0.20980</td>\n",
" <td>0.86630</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>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>0.19800</td>\n",
" <td>0.10430</td>\n",
" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
" <td>...</td>\n",
" <td>22.540</td>\n",
" <td>16.67</td>\n",
" <td>152.20</td>\n",
" <td>1575.0</td>\n",
" <td>0.13740</td>\n",
" <td>0.20500</td>\n",
" <td>0.4000</td>\n",
" <td>0.1625</td>\n",
" <td>0.2364</td>\n",
" <td>0.07678</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>564</th>\n",
" <td>21.56</td>\n",
" <td>22.39</td>\n",
" <td>142.00</td>\n",
" <td>1479.0</td>\n",
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" <td>0.11590</td>\n",
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" <td>25.450</td>\n",
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" <td>0.21130</td>\n",
" <td>0.4107</td>\n",
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" <td>0.3215</td>\n",
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" <td>18.980</td>\n",
" <td>34.12</td>\n",
" <td>126.70</td>\n",
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" <th>567</th>\n",
" <td>20.60</td>\n",
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" <th>568</th>\n",
" <td>7.76</td>\n",
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" --disabled-fill-color: #666;\n",
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" .colab-df-quickchart {\n",
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" border-radius: 50%;\n",
" cursor: pointer;\n",
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"\n",
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" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
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" animation:\n",
" spin 1s steps(1) infinite;\n",
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"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
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" border-left-color: var(--fill-color);\n",
" }\n",
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" border-color: transparent;\n",
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" border-bottom-color: var(--fill-color);\n",
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" 90% {\n",
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" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
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" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
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]
},
"metadata": {},
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}
]
},
{
"cell_type": "code",
"source": [
"data.frame.head()"
],
"metadata": {
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"base_uri": "https://localhost:8080/",
"height": 270
},
"id": "5yKcS-Hh5fal",
"outputId": "558ccaee-627a-48b3-d854-efe6eb37c747"
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"outputs": [
{
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"data": {
"text/plain": [
" mean radius mean texture mean perimeter mean area mean smoothness \\\n",
"0 17.99 10.38 122.80 1001.0 0.11840 \n",
"1 20.57 17.77 132.90 1326.0 0.08474 \n",
"2 19.69 21.25 130.00 1203.0 0.10960 \n",
"3 11.42 20.38 77.58 386.1 0.14250 \n",
"4 20.29 14.34 135.10 1297.0 0.10030 \n",
"\n",
" mean compactness mean concavity mean concave points mean symmetry \\\n",
"0 0.27760 0.3001 0.14710 0.2419 \n",
"1 0.07864 0.0869 0.07017 0.1812 \n",
"2 0.15990 0.1974 0.12790 0.2069 \n",
"3 0.28390 0.2414 0.10520 0.2597 \n",
"4 0.13280 0.1980 0.10430 0.1809 \n",
"\n",
" mean fractal dimension ... worst texture worst perimeter worst area \\\n",
"0 0.07871 ... 17.33 184.60 2019.0 \n",
"1 0.05667 ... 23.41 158.80 1956.0 \n",
"2 0.05999 ... 25.53 152.50 1709.0 \n",
"3 0.09744 ... 26.50 98.87 567.7 \n",
"4 0.05883 ... 16.67 152.20 1575.0 \n",
"\n",
" worst smoothness worst compactness worst concavity worst concave points \\\n",
"0 0.1622 0.6656 0.7119 0.2654 \n",
"1 0.1238 0.1866 0.2416 0.1860 \n",
"2 0.1444 0.4245 0.4504 0.2430 \n",
"3 0.2098 0.8663 0.6869 0.2575 \n",
"4 0.1374 0.2050 0.4000 0.1625 \n",
"\n",
" worst symmetry worst fractal dimension target \n",
"0 0.4601 0.11890 0 \n",
"1 0.2750 0.08902 0 \n",
"2 0.3613 0.08758 0 \n",
"3 0.6638 0.17300 0 \n",
"4 0.2364 0.07678 0 \n",
"\n",
"[5 rows x 31 columns]"
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"<table border=\"1\" class=\"dataframe\">\n",
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" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
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" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
" <th>mean fractal dimension</th>\n",
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" <th>worst texture</th>\n",
" <th>worst perimeter</th>\n",
" <th>worst area</th>\n",
" <th>worst smoothness</th>\n",
" <th>worst compactness</th>\n",
" <th>worst concavity</th>\n",
" <th>worst concave points</th>\n",
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" <th>target</th>\n",
" </tr>\n",
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" <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",
" <td>...</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",
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" <th>1</th>\n",
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" <td>17.77</td>\n",
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" <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",
" <td>...</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",
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" <td>21.25</td>\n",
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" <td>1203.0</td>\n",
" <td>0.10960</td>\n",
" <td>0.15990</td>\n",
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" <td>0.12790</td>\n",
" <td>0.2069</td>\n",
" <td>0.05999</td>\n",
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" <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",
" <td>0</td>\n",
" </tr>\n",
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" <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",
" <td>...</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",
" <td>0</td>\n",
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" <td>0.1809</td>\n",
" <td>0.05883</td>\n",
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" <td>16.67</td>\n",
" <td>152.20</td>\n",
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" <td>0.1625</td>\n",
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" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
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" @keyframes spin {\n",
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" }\n",
" 20% {\n",
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" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
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" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
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]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"source": [
"data.frame.info()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
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"id": "R6JKJVMC5kjM",
"outputId": "05b6895d-f338-41bf-d1ba-15fd0357b406"
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"execution_count": 7,
"outputs": [
{
"output_type": "stream",
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" 12 perimeter error 569 non-null float64\n",
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" 14 smoothness error 569 non-null float64\n",
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" 30 target 569 non-null int64 \n",
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},
{
"cell_type": "code",
"source": [
"data.frame.isna().sum()"
],
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"base_uri": "https://localhost:8080/"
},
"id": "aIGX7Fiw5sRt",
"outputId": "2ca20d6c-4150-41c2-e17c-a279f6273359"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"mean radius 0\n",
"mean texture 0\n",
"mean perimeter 0\n",
"mean area 0\n",
"mean smoothness 0\n",
"mean compactness 0\n",
"mean concavity 0\n",
"mean concave points 0\n",
"mean symmetry 0\n",
"mean fractal dimension 0\n",
"radius error 0\n",
"texture error 0\n",
"perimeter error 0\n",
"area error 0\n",
"smoothness error 0\n",
"compactness error 0\n",
"concavity error 0\n",
"concave points error 0\n",
"symmetry error 0\n",
"fractal dimension error 0\n",
"worst radius 0\n",
"worst texture 0\n",
"worst perimeter 0\n",
"worst area 0\n",
"worst smoothness 0\n",
"worst compactness 0\n",
"worst concavity 0\n",
"worst concave points 0\n",
"worst symmetry 0\n",
"worst fractal dimension 0\n",
"target 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"data.frame.describe()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 384
},
"id": "_0PZ2_lr51sn",
"outputId": "cbf26383-8d2d-488a-deaf-fb301898f06e"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" mean radius mean texture mean perimeter mean area \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 14.127292 19.289649 91.969033 654.889104 \n",
"std 3.524049 4.301036 24.298981 351.914129 \n",
"min 6.981000 9.710000 43.790000 143.500000 \n",
"25% 11.700000 16.170000 75.170000 420.300000 \n",
"50% 13.370000 18.840000 86.240000 551.100000 \n",
"75% 15.780000 21.800000 104.100000 782.700000 \n",
"max 28.110000 39.280000 188.500000 2501.000000 \n",
"\n",
" mean smoothness mean compactness mean concavity mean concave points \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 0.096360 0.104341 0.088799 0.048919 \n",
"std 0.014064 0.052813 0.079720 0.038803 \n",
"min 0.052630 0.019380 0.000000 0.000000 \n",
"25% 0.086370 0.064920 0.029560 0.020310 \n",
"50% 0.095870 0.092630 0.061540 0.033500 \n",
"75% 0.105300 0.130400 0.130700 0.074000 \n",
"max 0.163400 0.345400 0.426800 0.201200 \n",
"\n",
" mean symmetry mean fractal dimension ... worst texture \\\n",
"count 569.000000 569.000000 ... 569.000000 \n",
"mean 0.181162 0.062798 ... 25.677223 \n",
"std 0.027414 0.007060 ... 6.146258 \n",
"min 0.106000 0.049960 ... 12.020000 \n",
"25% 0.161900 0.057700 ... 21.080000 \n",
"50% 0.179200 0.061540 ... 25.410000 \n",
"75% 0.195700 0.066120 ... 29.720000 \n",
"max 0.304000 0.097440 ... 49.540000 \n",
"\n",
" worst perimeter worst area worst smoothness worst compactness \\\n",
"count 569.000000 569.000000 569.000000 569.000000 \n",
"mean 107.261213 880.583128 0.132369 0.254265 \n",
"std 33.602542 569.356993 0.022832 0.157336 \n",
"min 50.410000 185.200000 0.071170 0.027290 \n",
"25% 84.110000 515.300000 0.116600 0.147200 \n",
"50% 97.660000 686.500000 0.131300 0.211900 \n",
"75% 125.400000 1084.000000 0.146000 0.339100 \n",
"max 251.200000 4254.000000 0.222600 1.058000 \n",
"\n",
" worst concavity worst concave points worst symmetry \\\n",
"count 569.000000 569.000000 569.000000 \n",
"mean 0.272188 0.114606 0.290076 \n",
"std 0.208624 0.065732 0.061867 \n",
"min 0.000000 0.000000 0.156500 \n",
"25% 0.114500 0.064930 0.250400 \n",
"50% 0.226700 0.099930 0.282200 \n",
"75% 0.382900 0.161400 0.317900 \n",
"max 1.252000 0.291000 0.663800 \n",
"\n",
" worst fractal dimension target \n",
"count 569.000000 569.000000 \n",
"mean 0.083946 0.627417 \n",
"std 0.018061 0.483918 \n",
"min 0.055040 0.000000 \n",
"25% 0.071460 0.000000 \n",
"50% 0.080040 1.000000 \n",
"75% 0.092080 1.000000 \n",
"max 0.207500 1.000000 \n",
"\n",
"[8 rows x 31 columns]"
],
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>mean radius</th>\n",
" <th>mean texture</th>\n",
" <th>mean perimeter</th>\n",
" <th>mean area</th>\n",
" <th>mean smoothness</th>\n",
" <th>mean compactness</th>\n",
" <th>mean concavity</th>\n",
" <th>mean concave points</th>\n",
" <th>mean symmetry</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>count</th>\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",
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" <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",
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" <td>569.000000</td>\n",
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" <tr>\n",
" <th>mean</th>\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",
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" <td>0.062798</td>\n",
" <td>...</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",
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" <tr>\n",
" <th>std</th>\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>0.007060</td>\n",
" <td>...</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",
" <td>0.483918</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\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>0.049960</td>\n",
" <td>...</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",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\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>0.057700</td>\n",
" <td>...</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",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\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>0.061540</td>\n",
" <td>...</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",
" <td>1.000000</td>\n",
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" <tr>\n",
" <th>75%</th>\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>0.066120</td>\n",
" <td>...</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",
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" <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>0.097440</td>\n",
" <td>...</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",
" <td>1.000000</td>\n",
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" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"cell_type": "code",
"source": [
"data.data.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LSnXRqLV545s",
"outputId": "cd634c43-6e30-435e-9437-a4fcd61dc7fe"
},
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(569, 30)"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X = data.data\n",
"y = data.target\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .5)"
],
"metadata": {
"id": "sU86mWwq6Aml"
},
"execution_count": 18,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn import svm\n",
"from sklearn import metrics\n",
"\n",
"kernels = [\"linear\", \"rbf\", \"sigmoid\"]\n",
"gammas = [1, .01, .001, .0001, .00001]\n",
"\n",
"for kernel in kernels:\n",
" for gamma in gammas:\n",
" model = svm.SVC(kernel=kernel, gamma=gamma)\n",
" model.fit(X_train, y_train)\n",
" predictions = model.predict(X_test)\n",
" print(f\"{kernel} : Gamma: {gamma}: Accuracy:\", metrics.accuracy_score(y_test, predictions))\n",
" print(f\"{kernel} : Gamma: {gamma}: Precision:\", metrics.accuracy_score(y_test, predictions))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "i17jZMSt6Nl7",
"outputId": "b4b80d3b-b42a-4571-9f29-01b5d2e8c390"
},
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"linear : Gamma: 1: Accuracy: 0.9415204678362573\n",
"linear : Gamma: 1: Precision: 0.9415204678362573\n",
"linear : Gamma: 0.01: Accuracy: 0.9415204678362573\n",
"linear : Gamma: 0.01: Precision: 0.9415204678362573\n",
"linear : Gamma: 0.001: Accuracy: 0.9415204678362573\n",
"linear : Gamma: 0.001: Precision: 0.9415204678362573\n",
"linear : Gamma: 0.0001: Accuracy: 0.9415204678362573\n",
"linear : Gamma: 0.0001: Precision: 0.9415204678362573\n",
"linear : Gamma: 1e-05: Accuracy: 0.9415204678362573\n",
"linear : Gamma: 1e-05: Precision: 0.9415204678362573\n",
"rbf : Gamma: 1: Accuracy: 0.6023391812865497\n",
"rbf : Gamma: 1: Precision: 0.6023391812865497\n",
"rbf : Gamma: 0.01: Accuracy: 0.5964912280701754\n",
"rbf : Gamma: 0.01: Precision: 0.5964912280701754\n",
"rbf : Gamma: 0.001: Accuracy: 0.9239766081871345\n",
"rbf : Gamma: 0.001: Precision: 0.9239766081871345\n",
"rbf : Gamma: 0.0001: Accuracy: 0.9415204678362573\n",
"rbf : Gamma: 0.0001: Precision: 0.9415204678362573\n",
"rbf : Gamma: 1e-05: Accuracy: 0.935672514619883\n",
"rbf : Gamma: 1e-05: Precision: 0.935672514619883\n",
"sigmoid : Gamma: 1: Accuracy: 0.6023391812865497\n",
"sigmoid : Gamma: 1: Precision: 0.6023391812865497\n",
"sigmoid : Gamma: 0.01: Accuracy: 0.6023391812865497\n",
"sigmoid : Gamma: 0.01: Precision: 0.6023391812865497\n",
"sigmoid : Gamma: 0.001: Accuracy: 0.6023391812865497\n",
"sigmoid : Gamma: 0.001: Precision: 0.6023391812865497\n",
"sigmoid : Gamma: 0.0001: Accuracy: 0.6023391812865497\n",
"sigmoid : Gamma: 0.0001: Precision: 0.6023391812865497\n",
"sigmoid : Gamma: 1e-05: Accuracy: 0.5087719298245614\n",
"sigmoid : Gamma: 1e-05: Precision: 0.5087719298245614\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"model = svm.SVC(kernel=\"linear\")\n",
"model.fit(X_train, y_train)\n",
"predictions = model.predict(X_test)\n",
"print(f\"{kernel} : Gamma: {gamma}: Accuracy:\", metrics.accuracy_score(y_test, predictions))\n",
"print(f\"{kernel} : Gamma: {gamma}: Precision:\", metrics.accuracy_score(y_test, predictions))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "TPDP6md86-5g",
"outputId": "20c33fec-e1ca-46bb-c1c6-0e77ccca6770"
},
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"sigmoid : Gamma: 1e-05: Accuracy: 0.9508771929824561\n",
"sigmoid : Gamma: 1e-05: Precision: 0.9508771929824561\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import classification_report\n",
"\n",
"print(classification_report(y_test, predictions))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "nalgT_2K8LYf",
"outputId": "5b4e261d-2a0d-4e93-b47f-e26085f02758"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.93 0.93 0.93 97\n",
" 1 0.96 0.96 0.96 188\n",
"\n",
" accuracy 0.95 285\n",
" macro avg 0.95 0.95 0.95 285\n",
"weighted avg 0.95 0.95 0.95 285\n",
"\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import confusion_matrix\n",
"import pandas as pd\n",
"\n",
"pd.DataFrame(confusion_matrix(y_test, predictions))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 112
},
"id": "0xad03D88T5D",
"outputId": "c25ad5df-9b92-4ea8-d719-a2ca23c605ec"
},
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
" 0 1\n",
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"1 7 181"
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" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
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" const charts = await google.colab.kernel.invokeFunction(\n",
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" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 21
}
]
}
]
}
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