demo21_sonar.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>X0</th>\n",
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       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
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       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
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       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
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       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
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       "      <td>0.0177</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.2028</td>\n",
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       "      <td>0.2328</td>\n",
       "      <td>0.2684</td>\n",
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       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.1354</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>M</td>\n",
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       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>208 rows × 61 columns</p>\n",
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      "text/plain": [
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       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>R</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>R</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>0.0187</td>\n",
       "      <td>0.0346</td>\n",
       "      <td>0.0168</td>\n",
       "      <td>0.0177</td>\n",
       "      <td>0.0393</td>\n",
       "      <td>0.1630</td>\n",
       "      <td>0.2028</td>\n",
       "      <td>0.1694</td>\n",
       "      <td>0.2328</td>\n",
       "      <td>0.2684</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0098</td>\n",
       "      <td>0.0199</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>0.0193</td>\n",
       "      <td>0.0157</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>0.0323</td>\n",
       "      <td>0.0101</td>\n",
       "      <td>0.0298</td>\n",
       "      <td>0.0564</td>\n",
       "      <td>0.0760</td>\n",
       "      <td>0.0958</td>\n",
       "      <td>0.0990</td>\n",
       "      <td>0.1018</td>\n",
       "      <td>0.1030</td>\n",
       "      <td>0.2154</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0135</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0063</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0032</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0067</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>0.0522</td>\n",
       "      <td>0.0437</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0292</td>\n",
       "      <td>0.0351</td>\n",
       "      <td>0.1171</td>\n",
       "      <td>0.1257</td>\n",
       "      <td>0.1178</td>\n",
       "      <td>0.1258</td>\n",
       "      <td>0.2529</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0160</td>\n",
       "      <td>0.0029</td>\n",
       "      <td>0.0051</td>\n",
       "      <td>0.0062</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0138</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>0.0303</td>\n",
       "      <td>0.0353</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.0608</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.1354</td>\n",
       "      <td>0.1465</td>\n",
       "      <td>0.1123</td>\n",
       "      <td>0.1945</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0086</td>\n",
       "      <td>0.0046</td>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0035</td>\n",
       "      <td>0.0034</td>\n",
       "      <td>0.0079</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>0.0260</td>\n",
       "      <td>0.0363</td>\n",
       "      <td>0.0136</td>\n",
       "      <td>0.0272</td>\n",
       "      <td>0.0214</td>\n",
       "      <td>0.0338</td>\n",
       "      <td>0.0655</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.1843</td>\n",
       "      <td>0.2354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0129</td>\n",
       "      <td>0.0047</td>\n",
       "      <td>0.0039</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0115</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 61 columns</p>\n",
       "</div>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "url1 = 'https://archive.ics.uci.edu/ml/machine-learning-databases/undocumented/connectionist-bench/sonar/sonar.all-data'\n",
    "df1 = pd.read_csv(url1, header=None, prefix=\"X\")\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(208, 61)\n(208, 60)\n(208,)\n"
     ]
    }
   ],
   "source": [
    "print(df1.shape)\n",
    "data, labels = df1.iloc[:,:-1], df1.iloc[:,-1]\n",
    "print(data.shape)\n",
    "print(labels.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       X0      X1      X2      X3      X4      X5      X6      X7      X8  \\\n0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n\n       X9  ...     X51     X52     X53     X54     X55     X56     X57  \\\n0  0.2111  ...  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180  0.0084   \n1  0.2872  ...  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140  0.0049   \n2  0.6194  ...  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316  0.0164   \n3  0.1264  ...  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050  0.0044   \n4  0.4459  ...  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072  0.0048   \n\n      X58     X59  Label  \n0  0.0090  0.0032      R  \n1  0.0052  0.0044      R  \n2  0.0095  0.0078      R  \n3  0.0040  0.0117      R  \n4  0.0107  0.0094      R  \n\n[5 rows x 61 columns]\n"
     ]
    }
   ],
   "source": [
    "df1.rename(columns={'X60':'Label'}, inplace=True)\n",
    "df1.Label = df1.Label.astype('category')\n",
    "print(df1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['M', 'M', 'M', 'M', 'M', 'R', 'M', 'M', 'M', 'R'], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "classifier1 = KNeighborsClassifier(n_neighbors=5)\n",
    "X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size=0.1)\n",
    "classifier1.fit(X_train,y_train)\n",
    "y_predict = classifier1.predict(X_test)\n",
    "y_predict[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7619047619047619"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier1.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[14,  3],\n       [ 2,  2]], dtype=int64)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "result_cm1 = confusion_matrix(y_test, y_predict)\n",
    "result_cm1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.55813953, 0.64285714, 0.58536585, 0.53658537, 0.29268293])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "scores = cross_val_score(classifier1, data, labels, cv=5, groups=labels)\n",
    "scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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