{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Метод решающих деревьев для определения марки вина\n", "### Проанализируем ошибку классификации (долю неверно классифицированных объектов) на тестовой выборке. Не имеет смысла считать ошибку на обучающей выборке - в силу алгоритма обучения решающих деревьев она всегда равна нулю. " ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn import tree\n", "from milksets import wine\n", "from sklearn.utils import shuffle\n", "from sklearn.metrics import accuracy_score\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import random\n", "import numpy as np\n", "features, labels = wine.load()\n", "\n", "means, squares = [], []\n", "for a in range(1, len(labels)//2):\n", " test_accs = []\n", " for i in range(50):\n", " perm_features, perm_labels = shuffle(features, labels)\n", " train_features, train_labels = perm_features[:a], perm_labels[:a]\n", " test_features, test_labels = perm_features[a:], perm_labels[a:]\n", " \n", " clf = tree.DecisionTreeClassifier()\n", " clf = clf.fit(train_features, train_labels)\n", "\n", " test_answer = clf.predict(test_features)\n", " test_accs.append(1-accuracy_score(test_labels, test_answer))\n", " acc_mean =np.mean(test_accs)\n", " means.append(acc_mean)\n", " sq_err = 0\n", " for i in range(len(test_accs)):\n", " sq_err += (test_accs[i]-acc_mean)**2\n", " squares.append(np.sqrt(sq_err)/(len(test_accs)-1))\n", " \n", " \n", "\n", "#features, labels = shuffle(features, labels, random_state = 0)\n", "#print(features.shape,labels.shape)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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/B77q7n+rOGNmFxJKPC8Ck9z9mbz1/wO8E3jU3V9X5j5HAOPdfWmR9ZMIAbEJ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\nFlh/LaGEOxR4d4/lqm84Oy4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5fx9V4v/9lm7sP7kXXGzfiwj9mgw4qRvHCQdzb3f3BYSO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\nQUpEROqWgpSIiNQtBSkREalb/x/U4mnXYRBMxwAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.rcParams['font.family'] = 'serif'\n", "plt.rcParams['font.serif'] = 'FreeSerif'\n", "plt.rcParams['lines.linewidth'] = 2\n", "plt.rcParams['lines.markersize'] = 12\n", "plt.rcParams['xtick.labelsize'] = 24\n", "plt.rcParams['ytick.labelsize'] = 24\n", "plt.rcParams['legend.fontsize'] = 24\n", "plt.rcParams['axes.titlesize'] = 36\n", "plt.rcParams['axes.labelsize'] = 24\n", "plt.xlabel('Length of train and test set')\n", "plt.ylabel('Error on test set')\n", "plt.scatter(x = range(1, len(labels)//2), y = means)\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\user\\Anaconda3\\lib\\site-packages\\matplotlib\\font_manager.py:1297: UserWarning: findfont: Font family ['serif'] not found. Falling back to DejaVu Sans\n", " (prop.get_family(), self.defaultFamily[fontext]))\n" ] }, { "data": { "image/png": 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lnUa71xvJSvdTTzxweyLggpv/wbUNfnn3dx+23pdgaX9FK0iVb/OGexawck0v\nXUr/996goe0X3XfRH22j1Fgsh+w0lWWr1jBr3tN1j+lKjfah3IiiiarIezFt8viGx71t9w/9ViTN\nZwAiYsOyeU6aHa6RpFnkUmYrP3yDoVQx6MKbHuD6ex4v+8IUvRGMH5MG1T7xgO1B4rVf/8NQhzws\nlH5oRJ3LwPV0AZtPGsfTy7ufPcstTwiVX4JFtvnUM6tZ3RP9XqYuV692cyM1n2HtHxP3PbaUj1x2\nd1M/whrdXxHVBglo5gdKI/+jylrgX7jmXm75x1MNxzp2lNh3+02YOH4Mv7uvelwX3tz+s+xWJM07\ngD1IY2d+JyKWO2l2vkaSpkfo6LPLR68akfdBrTVKCa9Tam5XEvSb/NqRtFuhPK7eiLafZbdilJPv\nkuI+H1gqqfTNsoWkngKPNYUit7bzCB19Dt1580HvEN7WH2kEnaGOora+LiAf4+b7n6g6nmzpNfRE\n5yRMWDuuImGtWtPDd25+oF1h1U6aEfE14KPAE/SNkUnZ340+Or8WyQjjETr6nHjg9g13h2dmna83\n4No5jzc0mHwz6ia0iPhkRGwBbAFsn2cvzH8XeVgHGT+mWJJYn0fo2GubKRy+6+aD0gm8mQ2OVT29\n/Q7v16yGvikiYmHZ+Jg9EfFQkUfLo7YBKXJJcn0foaO/QbBFqugwSqLBt8zMOsC1cx/n/Zfe2fIz\nzqI/r18KHNPSCGzQFbkkORJG6Kg2CLaUKhS8co9p/OI/9+cX796fV7xgGuNHp49MV1kiHT+6i+dM\nGc/Y0fU/TmNHiS0njcvd5plZO63s7uW6uQu46+HFLd2u+55dz4zkdppDqUgbMqBu2bGjupix/cZs\nOHY0183tv5q9mVVXpPZ/2ztsl3QI8CZgb2Bqnr0QuB24NCJmNrVhG5CR2iNQJyhvI3rDvQtYuaaH\n8aNTu9CTDtyBPcuqwDdatlq7UzNr3IQxo7jn40f1W65tSVPSZsDFwOGlWRVFShu8FjguIp4otAMb\nkKJ9zzb6JW+doZHOG16681SWrmy+0wCz9YkED376lQ2Ua0PSlDQWuI3U6YGAW4EbgIdzka2BQ4GX\nkJLnXcCLI2J1wzuxARlpQ4NZdZU/iFZ09zBuVBcbbziGBUtXOZHaiNHqM82iQ4O9F9gTeAp4S0Rc\nW6XMRyUdCfwkl30PqYMEMxskpSHTvn7suvdy6l1hOPGA7fnb48s445d/bbhLuP+342Z86Mjng8S3\nf/cPrplCpT2tAAAgAElEQVTzWMfdh8097A268aPT/emNxo3hxntTP7itiGXcaLHpxNSF4HAYoKD0\nPkwcO5prB/E+fTtq/xc907wNmAG8MSJ+2U/ZY4D/Bf4cEfsNKEprmM80baAGWlGs2lnumC6xprex\nnl26lGo0r+7pXSuZI61z5pwG+171bN+zlX0Ml99mqHcfv15XcqXKWRuOH82Tyxq/aDZt8ni+ddw+\n69zmGMiIPOX1DT57zB6c/ou/VH097VBeSa38R0A9oyRettsWaezXBv4PjWr0h0eRLvXadXl2ETAG\nmBj9rCipC1gGdEfE5IZ3YgPipGmt0OqKYp1SY7u/s+zyxFx5j783omUDHfT3/pYnp5n3LWyoolij\nl+Eb+YHQyL77ez/r1YuotV69+/Gl4+6wXaYC4rp7WnsstStpPkNKgg3VEJG0GBhdPlKKtZeTprVK\nqyuKDfca261O/O2uiDeQHwhDWQmwkfelHcdSu5LmPcDzgRdFxO39lN0H+DNwX0Ts0vBO2kzSlsAZ\nwKuArYDFwJ+AL0XE9UOx3VzB6jTgrcCOwBrgHuD7wIX9ndWXc9K0Tjbca2wP98S/Pmn1sdSupPlF\n0lBhdwFHRsTCGuW2AK4GXgCcHxEfbHgnbSRpD1Jt303zrCXARFLPSAGcGRGfGcztSpqU190nz1pO\nqqA1Nj//DfC6iGhotBgnTbP2Gu6J36prV9LcHJgLbAwsAi4EZgKPAOOBbUld7Z0AbECqZbtrRAz5\n2FKSJpDO3rYD7gDeFhFzctI6C/gAKcEdFRHXDNZ2Jf2M1EnEU8DbgStIyfY44Fuk9/VTEfGRRuJx\n0jQzK66dnRvsC1wObEntCkwC5gOvjYg/F9pBm0g6jdT0ZRmwc0Q8UrH8MuC1wO0RsU+VTbR8u5Je\nSOpBCeDoiPhVxfJTgS8BK4Dpjfz4cNI0MyuuFYNQVxURfwJ2Bc4G/kpKnKWxMyPPOwvYrVMSZnZs\nnl5Smdiyz+fp3pJ2GqTtvjVP76tMmNkFpHujE4DXF4jJzMzaoKk71RGxKCI+HhF7kb7Qp+XHhIjY\nKyI+ERGLWhnoQEjaiL57hlfXKPZHUoICOGyQtvvSPK16OTgiVgA356eHNhKTmZm1z4Crd0VEd0Q8\nnh/drQiqDXahr4/cOdUKREQvcF9+umu7t6tUF33neutmcwvGZGZmbTJS6kRPK/v70TrlSsum1SnT\nqu1OAjasWN6KmMzMrE1GStIs71xhRZ1yy/N04iBst2UxSTpZ0ixJsxYurNoKyMzMWmCkJM31WkRc\nEBEzImLG1KlT+1/BzMyaMlKS5jNlf0+oU26DPF02CNttV0xmZtYmIyVplt8zfE6dcqVl8wdhu0vo\nS5ytjMnMzNpkpCTNe+nriGG3agXyqCyldpRzq5Vp5XZzf7L31Fs3K9WabTQmMzNrkxGRNCNiKVDq\nJueIGsX2A0pDmDXUcXsLtntjvXUljQcOLBKTmZm1T6GkKel2SbMl7dCugNrokjw9VlK15hulTuVn\nR8R9VZa3Y7s/ydOdJb2qyronkRLuCuCyAjGZmVkbFD3T3BV4XkQ80I5g2uzbwEPARsBvJO0KqVcf\nSZ+jr5u6M8tXkjRdUuTHCa3aLkBE3AFcmp9eJOkVed1Rko4HPpuXnd8Jnd6bmY10owuWfwTYvB2B\ntFtErJB0NOky597AHEnVhvBqeISTFm33JOC5pO74rpC0HBgFjMvLf0Pq59fMzIZY0TPNq4ENJO3X\njmDaLSLuAnYHvgI8QEpMT5KG4zqimbE0B7rdiFgC7A98mDROaQCrSH3Wvgt4TaNjaZqZWXsVHU/z\nOcCdpDPOIyLiiXYFZs3x0GBmZsU1OjRY0cuzOwIfAb4I3Cfph8CtwEKgp9ZKEXFTwf2YmZl1nKJJ\ncyZ97RIFnJIf9UQT+zEzM+s4RZPZP+lLmmZmZiNKoaQZEdPbFIeZmVnHGxE9ApmZmbWCk6aZmVmD\nmq6gI2ki8ApSg/7SII4LgduB30aEh7IyM7P1SuGkKUnAGcDppF5vqlkm6dPAZ6NIQ1AzM7MO1syZ\n5kXAcaQmJyuB2cDDednWpO7gNgI+CewCvH3AUZqZmXWAQklT0uuBt5GanZTOJJdUlJlE6hLudOA4\nSZdHhEfoMDOzYa9oRaCTSQnzfyLiI5UJE1JfqhFxJvBR0tnoyQMP08zMbOgVTZr7kLrL+3IDZb+c\ny/bbl5+ZmdlwUDRpbgQsjYjl/RWMiGeAJXkdMzOzYa9o0lwATMmjndQlaStgCqkZipmZ2bBXNGmW\nRis5Lzc9qee8PJ1ZcB9mZmYdqWjS/AKpItAbgZmSjpK0QWmhpE0lvUHSn4E3AL2kYcTMzMyGvaId\ntt8p6d3AN4ADgCuAkLQYGAdMyEVFSpjviYg7WxivmZnZkCnc92xEXAAcRN9l1y5gY2ADUrIEuAE4\nMJc1MzNbLzTV92xE3AIcJmlj4IWs3ffsHRHxdIviMzMz6xhFewTaI//5QEQsy8nxhtaHZWZm1nmK\nnmneSbpXuSXgUUzMzGxEKZo0FwO9EfFEO4IxMzPrZEUrAv0N2EjS+HYEY2Zm1smKJs0fkc5Oj29D\nLGZmZh2t6OXZrwOHAV+S1AN8PyJ6Wx+WmZlZ5ymaNL8LLALWABcAn5Y0i9TUpKfGOhER72w+RDMz\ns85QNGmeQOpGr9SJwWbAUf2sE4CTppmZDXtFk+a5pCRoZmY24hTte/acNsVhZmbW8Yr2CPSa/Oct\nbqtpZmYjTdHLs5eTKgFt0oZYzMzMOlrRpPkUQES4Cz0zMxtxinZuMAeYLGlSO4IxMzPrZEWT5gXA\nKOB9bYjFzMysoxWtPXuxpH2Bj+X+Z8+PiKfaE5qZmVlnKVp7tjR25nLgTOB0SX+n/x6BDms+RDMz\ns85QtCLQIVXW3zk/aumYzhDyvdj/Bo4BtgNWkMYI/WZE/Hywt53P1l8JvBzYF9gBGAM8Dtya1505\nkLjMzKx1iibNd7QlikEgaWvgJmD7PGsZMAk4FDhU0jcj4t2DvO1fA4eXPV8FdAPb5MebJH05Ik5r\nJi4zM2utovc0f9CuQNpJkoCfk5LaPODYiLgln+m9D/gM8J+S7oiICwdx22OA+4ELgV9HxL15m88F\nPg28EThV0t8i4htNvHQzM2shRXTM1dO2kfRa4DKgF9gnIu6sWH4+cBrwGLBdRKwejG1L2h+4LSLW\nuR+ck/F1pLPVByNih0bimTFjRsyaNavR8M3MDJA0OyJm9FeuaJOTyp1sIWmGpIMGsp1BcGyeXleZ\n1LIvkO69bklKUoOy7Yi4pVrCzMsC+GF+ur0k98JkZjbEmkqakv5N0l+AR4HbgBsqlk+RdK2k6yRt\n3II4B+qleXp1tYUR8Qip4wYonjTbue0ny/4eVXBdMzNrscJJU9KngUuA3YHVrD2+JgARsQh4gpRQ\n/m3gYTZP0ubApvnpnDpF5+bprp2w7ezgPH2c9H6amdkQKpQ0JR0JnA4sBd4MTCS10azmh6Rk+rKB\nBNgC08r+frROudKyaXXKDNq2JW0F/Ed+elGMhJvPZmYdruiZ5ntJZ5anR8Slte7HZb/PZfdsNrgW\n2bDs7xV1yi3P04lDvW1Jo4GLc/l/kmrS1it/sqRZkmYtXFjrN4yZmQ1U0aS5X57+uL+CEbEUWEKq\nAFOYpLMkrWny8clm9tlBvkq6NLsaeGtELK5XOCIuiIgZETFj6tSpgxKgmdlIVLRzgynAkoh4psHy\n6r9ITV00X/mlfL3yWCfUWWeDPC0y7FnLty3pU6TLsj2kNp9/KBCPmZm1UdEzzaeASZLqJQjg2Xty\nk0jtEwuLiHMiQk0+Ply2qfJ7jc+ps8vSsvkFwmzptiV9BDiDdFn7pIF27WdmZq1VNGn+KU9f3kDZ\n9+TpzQX30VIRsZC+mqe71Slaqtk6t06Ztm1b0n8Bn8hPT42I7zcah5mZDY6iSfM7pEuun5JU88xK\n0kmkzssD+Fbz4bXMjXl6RLWF+ay4lPSuH+xtS/pP4Lz89MMR8dWCMZiZ2SAolDQj4tekNprPB2ZL\n+jL5fp2kUyR9QdLdpETZRRql49YWx9yMS/L0SEnVavO+n/RjYD59SXBQti3p7cDX89NzI+KzBfdv\nZmaDpJkegU4AvgxsTuqQfKM8/3zgv+i7FPlF4JQBxtcq/0fquagLuEzSiwEkjZP0AVLfsABnV+t3\nVlLkxzmt3LakY4DvkpLq5yPi7AG+TjMza6OitWeJiDXAf0n6OvB24CWkRvtd9I0D+cOIuKeVgQ5E\nRISkN9A3fNetkpYB4+l7D75VdISTFmz78/TV9D1e0vF1dvX6iLilaHxmZtY6hZNmSUT8HfhoC2Np\nq4h4WNJepB6NXg9MJ/VsVBoo+n+HYNvlZ/pb9LObsc3GZ2ZmrTEihgYbSTw0mJlZcYMyNJiZmdlI\n4qRpZmbWICdNMzOzBjlpmpmZNchJ08zMrEFOmmZmZg1y0jQzM2uQ22muZyQtBB4qsMpm9I3UYq3l\n97Z9/N6210h8f7eLiKn9FWoqaUqaBJxIGtljG2BCRDy3bPlk4GjSKCc/DmfmjiVpViMNeq04v7ft\n4/e2vfz+1la4Gz1JLwF+Qer2TXn2WkkxIhZLej/wAmAhcNUA4zQzMxtyhe5pStoa+A2wJXA1cDzw\ndI3i3yYl1aMHEqCZmVmnKFoR6EPAxsDFEfGKiPgxsM5QWtmVefriZoOzQXHBUAewHvN72z5+b9vL\n728Nhe5pSvob8FzguRExL8+bD2weEaOqlF8OrI6IKa0J18zMbOgUPdPcBnimlDAb8AwwoeA+zMzM\nOlLRpLkKGCdJ/RWUNB6YAixqJjAzM7NOUzRp/o1U43a3Bsq+GhgF/LVoUNZekraU9GVJ/5C0UtLj\nkn4t6bChjq2TSdpW0mn5vfqnpFWSlkq6S9JnJE2rsd50SdHAY8RW8Zd0QgPvz7I664+V9N+S7pS0\nTNIiSbdKOrmRH/nrswaPvdLj4Ip1fexWKNrk5HJgBvAR4C21CuUvj8+TmqL8b9PRWctJ2gO4Adg0\nz1pCasj8KuCVks6MiM8MVXydStI2wDz6mllBeu82BPbIj5MlHRMRN9bZ1ON1lnUPNM71QDfwVI1l\nz1SbmduN3wDsk2ctJ90WenF+vFrS6yJiTYtjHS7qHXMAk0jv12rg7ia3M3KO3Yho+EH6gpgH9AA/\nAl4IPJafbwTsTqph+zjQS/oHjCmyDz/a9yB9MOaRfszcDuyW508CvpDn9wJHDnWsnfYApuf35jfA\nG4CN8/yxwMuBB/L7txjYssq6kT5uQ/9aOvEBnJDfo5lNrPuzvO6TpB9/Il3lejuwIi/75FC/xk59\nAHfm9+gXVZb52K18T5p4g3cBHsxfID01Hr3A34EdhvoF+rHW/+60/AFYCmxVZfllefnsoY610x7A\nZGDPOst3LvuCPrtimb94+n9/m0qa+Yd75Mdrqiw/NS9bTqrlP+SvtZMewF79vH8+disehTtsj4h7\ngD2BTwGP5F915Y8FwGeBfSLigaLbt7Y6Nk8viYhHqiz/fJ7uLWmnQYppWIiIxRFxV53l9wJ/zE/3\nqVXOWu6teXpfRPyqyvILSGf/E4DXD1pUw8fb83QB8NuhDGS4aGqUk4hYEhH/ExHbAtsC+wEvIZ1Z\nTouIMyJicSsDtYGRtBF9X+ZX1yj2R9IXDIArBRX3ZJ6u02bZ2ualeXpNtYURsQK4OT89dFAiGiYk\njabvR8clMXLv+RZStBu91+THZqV5EfFwRPw5Im6Lxttv2uDbhb5KLHOqFYiIXuC+/HTXwQhqfZG/\ngP5fflqzMkWu0blE0gpJD0r6saQDBifKYWE3SXPy+7NU0t2Szpe0fWXBXCt25/y06jGdzc1TH9Nr\nezmwef77B/0V9rGbFD3TvBz4ObCyDbFYe5U3h3i0TrnSsqrNJ6ym95D6ZO6l/hfQi3MZSPeLjgVu\nlvSlkd40ItuM9ANvOTCe1LztNGCOpLdWlJ1EqpwIPqabcUKe3hURdzZQ3scuxZPmU8CSiKjZXso6\n1oZlf6+oU255nk5sYyzrldyM59P56dciYm5FkZXAN4CDgI0idSu5Aely+a9zmVOBMwYh3E71KHA2\nqQb++IjYlHQMvpJ0pjgB+IGkg8rW8THdJEmbkGoaQ/0feT52KxRNmnOAybldlNmIl9skX076Up8N\nnF5ZJiIei4j3RMTNpR+ckdweEa+hry3zmZJGZD/NEXFNRJwbEXMiYnWetyoifgvsT6qNPxpwG+LW\neAupudQa4OJahXzsrqto0ryAVMnhfW2IxdqrvGF4vf6AN8hTX03oR/61fg2wPXA/8MqIaObWRSnR\nbogrYK0jVyr8VH764rI6FT6mm1eqNXtlRCwYwHZG3LFbKGlGxMXAV4GPSfp4/tKw4aH8ns9z6pQr\nLZvfxliGPUmTSbWQdwf+CRweEf31vFJVRDxIGqwdYIfWRLjeuS1PRfqRAqlHplLi9DHdIEm7AC/K\nT/utAFTPSDx2C3WjJ+mG/Ody4EzgdEl/J71pPTVWi4gYEb9AOty9pEbKIlWuuK+ygKQuoNQ+s/K+\nnGWSNiS1aZtB6hHr8Ij459BGNfJEREi6h/R/qNcfdqnWrI/p5IQ8fYq++5LWoKJ9zx5SZf2d6av2\nXU3jA3Za20TEUkmzSL8wjwB+WaXYfqSebwCuH6zYhhNJE0hfNPuT2mUeHhH3D3Cb2wNT89MHBxbh\nemu/sr/nlf19IylpHlFtJaXRlg7MT0f8MS1pFHBcfvqT0v3jAWxvxB27RZPmO9oShQ2WS0hJ81hJ\n50ZE5eWqD+bp7IhY50x0pJM0lvRj46WkIe+OjIh67QNL6yki6v14LN2vW0HqeHxE6e/9yRUPP5yf\n/ikiFpYt/gmpv+udJb0qIn5TsfpJpB+CK0jdRI50h9N3ubqRtpk+disNdT9+fgzeg7U7bJ8N7Jrn\nbwR8jr4+KN1h+7rv3ShSG+Ug3Ut7cYF1ZwL/TWp/2JXnidRv6mVl7/vZQ/06h+i9nQ7cQqqcslXZ\n/LHAUaThBYN0C+jQKuuXOmx/AnhF2f/reNKtJHfY3vdeXZLfjzkNlvexW/FQfhNshJC0J+kyVfnQ\nYBNJlcIC8NBgVeT2gb/LT1fS191gNf+KiFJFCyTNA7bLT7tJ7/kGrF3j86vAqTECP5CSprP2pb0V\npAo+k4Exed5y4D8i4kdV1q82NNgoYFx+/htgJA8NBjz7Pj1GOu5Oj4jPNbDOPHzsrsVJcwSStCWp\nMfKrgK1IH4Q/AedHxIi/71ONpENI988a8VBETC9b942ke277knoN2oQ0duHDwB+ACyLitirbGRHy\nfeITgQNIg0FMJfX28wypKc/1wDcj4qE62xgL/Bep/eGOpLPSucD3gQtHyhd6PZJOBC4k9eqzTUTU\n60WptI6P3QpNJ818g30v0vXxDVl7cN61RMQPm9qJmZlZBymcNHN1+8+Qqi1vUL90EhEe9cHMzIa9\nou00x5PuHcwgXf74C+lyymrS5b0tSJdGRGoD9NdWBmtmZjaUinaj925Sk4W/Ac+LiBfm+U9FxEER\nsROpt46fAFOA6yLipdU3ZWZmNrwUbaf5RlINyw/WuikfqWeUYyWtAc6VdHtEXDnAOM3MzIZcoXua\nkp4mtembEBHdeV4v8GRETK0ouw3wEHB1RLy8dSGbmZkNjaJJcwWwrDxBSnoGGBUR46uUfwrojogt\nWhGsmZnZUCp6T3M+69aYnQ+MyX0QPkvSGNJZ6WTMzMzWA0WT5oPA+HzpteTPeXpsRdnjSL1y/KvJ\n2MzMzDpK0aRZ6kasfKiv75KamJwl6euSTpL0FeBbpEpDlw48TLPWkTRPUuRefjqKpCMlXS9pkaTe\nHOcJQx1XoyRNzzGP+B54wO/H+qho7dmfAK8m9fF4EUBEXCfpa8B7gf8oKyvgVuATAw/TBkLSRaTO\nsH8XEYcMbTTtk/swPQFYFBFfGtJgmiDpQOBK0o/ZHtI4tUHqi7W/daczjF+7rUvSOfnPL0XEoqGM\nBYbnMSbpNFLzx4siYl4rtlkoaUYaN/BFVeafIum3pCYpW5M6s76WFGh3KwI1a8B04GxSre1h8aGu\ncAopYV4KnBAR/SbLMtPpjNfeTZUBzq0pZ+fpRaSh6IbadDrjGCviNFKH8zNZexzWphU906wpIq4C\nrmrV9sxGoN3y9EcFE2bHiIhHqD8ovdmwVvSeppm1T2m4pWVDGoWZ1eSkaXVJmijpTEl/lrRY0kpJ\n90v6SkUt6vJ1ZpYqsEiaIOkcSfdJWiFpgaSfSnpeP/t9laQb8z6XSPqjpLdXbr+s/Dz6hu7arlT5\nouxxwjo7SettIuk8SQ9KWiXpEUkXSprWxNtV2uY4Se+XdFuOf0V+/eflYdkqy5cqikzPs24si3tm\nA/ubR4OvvbwSlKStJH1D0gP5td9ZVm5rSR+UdFX+fy/P/4c7JH1M0pQasdSs+CLporzsHEmjJJ0m\n6a687ack/UbSjP5eb439bibp3ZL+T9K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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.xlabel('Length of train and test set')\n", "plt.ylabel('Square error of error on test set')\n", "plt.scatter(x = range(1, len(labels)//2), y = squares)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Как мы видим, ошибка классификации убывает с ростом размера выборок, а ее среднеквадратичное отклонение остается примерно постоянным с некоторого момента, что обосновывает пригодность модели." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0" } }, "nbformat": 4, "nbformat_minor": 2 }