9_集成学习.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d09eb5dc",
   "metadata": {},
   "source": [
    "## 使用随机森林的方法解决手写数字识别问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1a7e1688",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.datasets import fetch_openml\n",
    "import numpy as np\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a5bac87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9666666666666667\n"
     ]
    }
   ],
   "source": [
    "digits = load_digits()\n",
    "X, y = digits.data, digits.target\n",
    "\n",
    "# 数据集划分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1017)\n",
    "\n",
    "# 创建随机森林分类器\n",
    "rf_classifier = RandomForestClassifier(n_estimators=100, random_state=1017)\n",
    "\n",
    "rf_classifier.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_classifier.predict(X_test)\n",
    "\n",
    "# 计算准确率\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "88ac4339",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# 计算混淆矩阵\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "# 使用Seaborn库绘制热力图\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\", xticklabels=digits.target_names, yticklabels=digits.target_names)\n",
    "plt.title('混淆矩阵')\n",
    "plt.xlabel('预测值')\n",
    "plt.ylabel('真实值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d5a9a869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dOqZCn4nFixfH7Nmz44gjjohevXrFbbfd1uZ1OyLFew+WLVsW5eXlcfLJJ8f3v//9OOOMM6KoqCj3dxoREYMHD46XX345IiK3x+mkk06Kp556Kg4//PDo1On1uzfLsrc9iMGsWbNi8ODBccYZZ+SWderUKaqqquKEE07IfT3wwAO53De644474jvf+c4+3dbq6uro1KlTnHfeefu0Ph1Doc9Ez549o2vXrm2+PJgJyFfoMzFjxoxYu3ZtvPvd746VK1fG6aefHr/85S/bvD4dT6HPxOrVq2P8+PHx0ksvxYwZM+Kaa66JmpqaNq/f0Sjee1BRURF/+ctf4sEHH4wTTjghZs+eHX369InS0tLcZRobG6Nv3767rNujR4/46le/GkVFRbFjx4745z//mTvwwO4O9x/x+t9c33PPPbssnzJlSqxatSr3tfPACm92+OGH79MRCjds2BATJ06Mm2++eZcDM8AbdZSZgLYyE5Cv0Gfirrvuiq997Wtx++23x7Rp02L69Ol79e4gHU+hz8TKlStj4sSJ0bNnzzj//PNjxIgRMWvWrDav39E4uFobHXvssTFv3rzcR0Dq6+vj0EMPzR1A4M0DMHz48Ljkkkti27ZtsWrVqujZs2dEvH4Ovd2db6+oqCi6deu2y/VOnTo1br/99tz3GzZsyNuLtT9aW1vjoosuiqFDh8bw4cMPSCYdRyHOBOwPMwH5Cm0m1q1bF+ecc07u+w9+8IPxt7/9LRobG306hDYptJl4s5KSkli3bt0Bzy0U3vFuoyOPPDLKy8tzJ7ZfunRp9OnTJ/fzxsbG3L9XrFgRL7zwQgwdOjT+/Oc/xyOPPBIf+tCHYvDgwXH33XfH9u3bc3/j8Xauu+66eP7553NfB7IgX3PNNbFp06a44447DlgmHUchzgTsDzMB+QptJnr16hXbtm3Lfb9mzZo45phjlG7arJBmYvXq1fHRj340b9mSJUuirKxsv7MLleK9l/r16xc333xz3H333XH22WfH7Nmz46qrropLL700d5lvf/vbceWVV8ZRRx0VXbt2jV/96lfRrVu3GDhwYDQ0NMTnPve5+O53v9um65s6dWruSIfl5eV7/FuirVu35j0ZvJ3q6uq455574v7774/W1tZoaGjI/RKAvVEoMwEHipmAfIUyE1/4whfiuuuuixdeeCGee+65+OY3vxlDhw5t8/qwUyHMRFlZWWzYsCGuv/76WLFiRVRVVcWSJUtyR25nV4r3W6ivr4+1a9fmHWr/qKOOitmzZ8eyZcviyiuvjDlz5sTnP//53B6nRx55JB588MG48sorIyJiwoQJMWTIkLjyyivjox/9aIwdOzZKSkqiZ8+eeXu1dmptbY3m5uZoaWmJiD3voXpzSb788svjG9/4Rptv2/Tp02PLli3Rv3//6NatW3Tr1i1v2GF3CnkmYF+YCchXyDNRVVUVH/vYx+ITn/hEfPjDH44TTjghbr755r27g+hwCnUmioqKYvbs2fHb3/42PvKRj8Tvfve7mD9/vrMkvZWMPZo0aVLWq1ev7LbbbsuyLMuWLl2a9e/fPxs8eHD2j3/8I3e5O++8MystLc3q6+uzxYsXZ1OnTs2yLMvuu+++7Nhjj83Wr1+fZVmWbdq0KTv33HOzurq6rKqqKuvSpUt266235l3nxz/+8ayqqirr0aNH1rt376y0tHSXr969e2d9+/b9z9wJ8AZmAvKZCchnJiCfmWCnoizLsvYu/weLHTt2RE1NTXz4wx/e5WevvPJK9OrVK29ZlmWxYcOG3Z4D9dVXX42mpqa8oxrCwcZMQD4zAfnMBOQzEx2X4g0AAAAJ+RtvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEOr/9RV5XVFSUbCNKSkqS5M6ZMydJbkTEmWeemSx7zZo1ybKrq6uT5E6ePDlJbmr7c2zBlDNxMBo6dGiy7JSzfMMNNyTJNRMHj5SP3RkzZiTJra2tTZIbEXHrrbcmy547d26y7JT2dS4O1plIJeXv8uXLlyfLPlh/n6fyTn2eSNUnIg7eTpFKfX19suyU/48ptWUuvOMNAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAl1bu8NAID2dMMNNyTLLi0tPahyIyKWL1+eLHvu3LnJsjkwhg4delBmL1iwIFk2B4cxY8Ykyz7zzDOTZc+YMSNJ7pw5c5LkRkTU1dUlyy5k74jiPWDAgCS5KYck5Qu1srKyZNmVlZVJclMOd8oXgR3NWWedlSw75WMAAAAOZj5qDgAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJdW7vDYiIWLBgQZLcoqKiJLmpDR06NFn26NGjk2Vz4JSVlSXJnTNnTpJcOJjV1tYmy+7fv3+ybEjhM5/5TLLsNWvWJMuePn16smwODgMGDEiWXV9fnyx78uTJSXLr6uqS5LLvvOMNAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJBQ5/begINVSUlJsuyrr746WXZ9fX2S3OXLlyfJ7ajKysqS5E6fPj1JbkTEWWedlSz7zDPPTJa9bNmyZNlwMKqtrW3vTaAdpXr+iYiYM2dOsuyUr8tS3SdeOx1YKR8DKVVWVibJraurS5IbEVFdXZ0sO+V2tzfveAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQp3bewMAoD3V1dW19ya8o8yZM6e9N4F2VFZWdlBmjxkzJln2UUcdlST36quvTpIbETF9+vRk2e9UlZWVybJT/l68/vrrk2WnMnny5GTZo0ePTpY9d+7cZNltoXjvo5S/0M4888xk2cOGDUuWzYGzYMGCgyo3Iu2LnpQzMXbs2CS57f3LHQCAdw4fNQcAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhDq39wYAQHsaMGBAe2/CXquvr0+WXVtbmyybd74FCxYkyx49enSy7JQzsXDhwiS5w4YNS5IbETF9+vRk2e9UKX93HYzPEyktX748WfaMGTOSZc+dOzdZdlsUdPEeOnRosuyUTx733Xdfsuz2fsDBO01JSUl7bwIAAAXOR80BAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAIKHO7b0BERElJSVJcmfMmJEkNyKivr4+WfaCBQuSZVdXVyfJTfV/GBExbNiwZNkwYMCAJLkpZ6Kuri5ZdkeU8v8qldra2vbeBApUytcgo0ePTpadcibKysqS5Jrjg0eqx0BEute5Y8aMSZIbEdG/f/9k2Sn7W3vzjjcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKd23sDIiLq6uoOqtyIiNLS0mTZ9957b7LsNWvWJMmtrq5OkhsRUVJSkiybA6e2tjZZdn19fbLsBQsWJMlN+bhN+butI5o+fXqy7DFjxiTJ7d+/f5LciIjJkycflNkcGCmfzwcMGJAsO9WspVRZWdnem0AbpXqtEJGuUyxcuDBJbkTEsGHDkmXPnTs3WXZ78443AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCRVmWZe29EQAAAFCovOMNAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3nupqakpduzYsVfrbN68OcaOHRt///vf23T5TZs25X3f3NwcDQ0Ne3Wd8J9iJiCfmYB8ZgLymYmOSfF+C01NTdHa2pq3bNSoUfGd73wnb1lzc3O0tLTsMaekpCSeeuqpuPfee9/2OhsbG6Nv374xd+7c3LInn3wyunfvHtu2bdvLWwAHlpmAfGYC8pkJyGcm2Klze2/AO9m1114bTz/9dBxyyCG5ZUuXLo0XX3wxHn744dyy5ubmuPbaa+P888/Prffzn/88L2vjxo0xZcqU+OEPf5i3fMqUKXHxxRfnvp83b1507949Nm/eHH369Ini4uLYtm1btLS0xAc+8IGIiNi+fXtMnDgxvvKVr+zzbdu0aVO85z3vyVt2//33x8iRI/c5k8JXyDOx07Rp0+L555+P6urq/c6i8JkJyFfoM/E///M/cccdd0SvXr3ipz/9afTr12+/8ih8hT4TOz399NMxcuTI+Otf/3pA8gqR4v0Wbr311oiImDVrVmRZFhERl156aYwdOzbe9773RURE165dY8iQIXnrbdmyJYYNGxbTp0/PW7527do48sgjo6SkJCIiBgwYEI2NjbmfZ1kWN954Y1xxxRUxcuTIGDVqVBQXF8fMmTPjzjvvjGeeeSYiIlpaWqKoqGi/btvSpUtj0KBBMW/evNyyd73rXfuVSeEr5JmIiJg5c2Zce+21dkDRZmYC8hXyTNx1111x1113xbx582Lz5s0xYsSIWL58eXTp0mW/cilshTwTO7W0tMQll1yy1x+f72h81LwNLrzwwnj22Wdj1apVMXHixGhsbIxVq1bFE088EV/+8pd3uXxFRUWcdtpp8Yc//CEGDRoUL730UkRE3HfffXH22WfnLvelL30pb0/pT37yk1i5cmUce+yx0blz56iqqoqKioqYNGlS/PGPf4yKioqoqKiIuXPnRufO+7fPZPHixTFw4MAoKSnJfb1xTxy8lUKciQULFsQtt9wSY8eO3a8cOiYzAfkKcSbuvPPOmDBhQgwaNCjOO++8OOmkk+LJJ5/cr0w6jkKciZ1uuukmpbsNvOPdBl26dIm5c+fuskdz27Ztceihh+5y+XHjxkVra2tkWRZDhgyJU089NW6//fYoKiqKsrKy3OXe+NGONWvWxIQJE+KYY47JLXvllVdi/Pjxcdlll+WWXXbZZbFly5ZdrnPMmDFRUlKyy16xPVm8eHG8+OKLcdttt8XRRx8d11xzTVxxxRVtWhcKcSZOPPHEqKmpiZtuuilqa2vbtA7sZCYgX6HNRJZlsXLlyrj77rtzy3aWok984hNvuz4U2kzs9NJLL8X06dPjV7/6VYwbN67N63VEincbjR8/Prp375637OWXX44HHnhgl8tmWRYf+chH4gtf+EJ8/etfjzPOOCOKi4vjT3/6U+4jJW82a9asGDx4cDQ1NeWWderUKaqqqmLatGm5ZRs3bowzzjhjl/XvuOOOvfq4yOrVq2P8+PFx0UUXRU1NTVx00UVx6qmn7jYbdqfQZqJnz55tvizsjpmAfIU0Ew0NDbFjx47o06dPbtmRRx6ZexcS2qKQZmKnSy+9NCZNmhTHH3/8Xq3XESnebbRu3brYunVr3rK//e1vu71sUVFRXHfddTFu3LhYtGhR/OIXv4iIiHvvvXePB+G4+uqro6GhIUaNGpW3fMqUKbvsodqdww8/vM23JSJi5cqVuX+ff/75MWLEiJg1a5biTZsV2kzA/jITkK+QZmLnR3Lf+M5k165dd7l98FYKaSYiIn784x/Htm3b4qqrroq1a9fu1bodkeLdRo888shuPxry5r1CO3bsiObm5vjUpz4VS5Ysicceeyz3sw0bNsSnP/3p3PctLS3R0tIShx9+eBQVFUW3bt12ud6pU6fG7bffnpeRohyXlJTEunXrDnguhavQZwL2lpmAfIU0E4cddlgcdthhsXHjxjjyyCMjIuK1115zYDX2SiHNxD/+8Y+YNGlSPPbYY9Gpk8OGtYXivQfbt2+P7du3R9euXSMi4qGHHooTTjgh7zI1NTW5I702NzdHcXFxvPjii3HqqadGly5dori4OCIiJk6cGK2trVFXVxdLliyJSy65JCJeH5SePXvGn/70pz1ux3XXXdemPVR7Y/Xq1TF69Oi8A4IsWbIkBg0atN/ZFK5CngnYF2YC8hX6TFRUVERNTU307ds3IiKWL18eJ5544gHJpjAV8kw8/PDDsXHjxhg4cGBEvL6zoKGhIUpKSuK5556L3r177/d1FBrFew+eeOKJuOCCC6K4uDi6du36lnuF3v3ud0dLS0s89dRT0a9fv/j3v/+9y2Uuu+yyaGhoiMrKyjj55JPb/FGOtu6h2rp1axQVFcVhhx32tpllZWWxYcOGuP766+P888+PWbNmxZIlS5yjlbdUyDMB+8JMQL5Cn4kLLrggvvvd78bQoUPj1VdfjV//+texcOHCNq1Lx1TIM/HZz342zjzzzNz369atixEjRsSiRYscI2QPFO89+OQnPxn19fUR8fp59KZNmxZHHHFEXHPNNRHx+rkcn3vuuRg+fHjeg+7NVq5cGV//+tfjtddei9/85jcxYsSIWLFiRdxwww0xbty43F6snVpbW6O5uTlaWloiYs97qJqamvL+zujyyy9v81EIi4qKYvbs2TFu3Li45ZZbory8PObPn7/LHjh4o0KeCdgXZgLyFfpMXHrppfG///u/cdxxx0VTU1OMHTs2PvShD7VpXTqmQp6JI444Io444oi8ZZ07d8474jpvkrFHzz33XHbVVVdlxx13XPblL385e/nll3M/27RpU/aDH/wgKy0tzcrLy7NFixblfjZ//vxs8uTJ2YABA7Kjjz46mzJlStbc3Jz7+e9///vslFNOyU466aTs0UcfzbvOj3/841lVVVXWo0ePrHfv3llpaekuX71798769u2b/g6ANzETkM9MQL5Cn4nt27dnjz/+ePbUU0/tdxYdQ6HPBG2neL+F559/PvvRj36UbdmyZY+XaWpqyu65557s3//+d27Zww8/nA0fPjx74IEHsoaGht2u19zcnH3rW9/Knn766QO+3ZCKmYB8ZgLymQnIZybYqSjLsqy933UHAACAQuXY73upqakpWltb85ZlWZZ3ovq2yrIsduzYscvyTZs25X3f3NwcDQ0Ne50P/wlmAvKZCchnJiCfmeiYvOP9Fo477rjo1q1bdO3aNerr6+Nzn/tcrF+/PpYtWxbNzc2xfv36OOmkk3Ln2vvzn/8cO3bsiGHDhsWXvvSlOPfcc2PhwoXxxrv4mGOOife///0RETF9+vR49tln44EHHsj9vLGxMXr06BHV1dUxdOjQiIh47LHH4rzzzot//etfjkZLuzITkM9MQD4zAfnMBDs5qvlbWL9+fURE1NbWxmmnnRajR4+OU045JSJePw/f9773vXjiiSfy1unUqVOMHTs2Lr744pg9e3YMGTIkLrzwwoiI+Mtf/hLHH398TJkyJTZv3hyHHnpo3pEEIyLmzZsX3bt3j82bN0efPn2iuLg4tm3bFi0tLfGBD3wgIl4/J+DEiRPjK1/5yj7ftk2bNsV73vOevGX3339/7jyCsDuFPBM7TZs2LZ5//nmn16NNzATkK/SZ+J//+Z+44447olevXvHTn/40+vXrt195FL5Cn4mdnn766Rg5cmT89a9/PSB5hUjxfguNjY0xderUePnll+OGG27IDUnE6+e/29Pptz7zmc/E6aefHj169IjDDjssvv/978fatWujpqYmFi1aFPPnz49FixbF6aefnrdelmVx4403xhVXXBEjR46MUaNGRXFxccycOTPuvPPOeOaZZyIioqWlJYqKivbrti1dujQGDRoU8+bNyy1717vetV+ZFL5CnomIiJkzZ8a1115rBxRtZiYgXyHPxF133RV33XVXzJs3LzZv3hwjRoyI5cuXR5cuXfYrl8JWyDOxU0tLS1xyySW7/cg7/z9/4/02Vq5cGY899lh88YtfjIiImpqaKC8vj0mTJsWcOXOivLw8ysvL4+67746I199JnjlzZvTs2TP3YF6/fn1UVFTkMouLi3c5315ExE9+8pNYuXJlHHvssdG5c+eoqqqKioqKmDRpUvzxj3+MioqKqKioiLlz50bnzvu3z2Tx4sUxcODAKCkpyX0dcsgh+5VJx1CoM7FgwYK45ZZbYuzYsfuVQ8djJiBfoc7EnXfeGRMmTIhBgwbFeeedFyeddFI8+eST+5VJx1CoM7HTTTfdpHS3geK9B62trVFUVBSzZs2KcePGRXNzc0S8/rGM9773vfHqq6/G448/Hs8++2wMGTIkXnvttYiI2LhxY0yePDkuv/zy3EETunbtGiUlJW95fWvWrIkJEybEMccck1v2yiuvxPjx42Pt2rVRX18fS5cujYqKitiyZcsu648ZMyYqKyvbfPsWL14cs2fPjiOOOCJ69eoVt912W5vXpWMq9Jk48cQTo6amJo477rg2r0PHZiYgXyHPRJZlsXLlyvh//+//5Zaddtpp8Yc//KFN69MxFfJM7PTSSy/F9OnT4/bbb9+r9ToixXsPli1bFuXl5XHyySfH97///TjjjDOiqKgo93caERGDBw+Ol19+OSIit8fppJNOiqeeeioOP/zw6NTp9bs3y7K3PYjBrFmzYvDgwXHGGWfklnXq1CmqqqrihBNOyH098MADudw3uuOOO+I73/lOm2/f6tWrY/z48fHSSy/FjBkz4pprromampo2r0/HU+gz0bNnz+jatWubLw9mAvIV8kw0NDTEjh07ok+fPrllRx55ZN5tgzcr5JnY6dJLL41JkybF8ccfv1frdUSK9x5UVFTEX/7yl3jwwQfjhBNOiNmzZ0efPn2itLQ0d5nGxsbo27fvLuv26NEjvvrVr0ZRUVHs2LEj/vnPf+YOZLanj2FcffXVcc899+yyfMqUKbFq1arc184DK7zZ4YcfvldHKFy5cmVMnDgxevbsGeeff36MGDEiZs2a1eb16XgKfSZgb5kJyFfIM7HzI7lvPIhV165dY+vWrW1an46pkGciIuLHP/5xbNu2La666qo2r9ORObhaGx177LExb9683EdA6uvr49BDD80dkOzNAzB8+PC45JJLYtu2bbFq1aro2bNnRLx+Dr3dnW+vqKgounXrtsv1Tp06Ne+jGxs2bMjbi3WglJSUxLp16w54LoWr0GcC9paZgHyFNBOHHXZYHHbYYbFx48Y48sgjIyLitddec2A19kohzcQ//vGPmDRpUjz22GO7ffecXbmX2ujII4+M8vLy3Intly5dmvdxo8bGxty/V6xYES+88EIMHTo0/vznP8cjjzwSH/rQh2Lw4MFx9913x/bt23N/4/F2rrvuunj++edzX8OHD9/v27J69er46Ec/mrdsyZIlUVZWtt/ZdByFNBNwIJgJyFdoM1FRUZH3Z3nLly93DAT2SiHNxMMPPxwbN27MHay5X79+sXbt2igpKYm1a9fud34hUrz3Ur9+/eLmm2+Ou+++O84+++yYPXt2XHXVVXHppZfmLvPtb387rrzyyjjqqKOia9eu8atf/Sq6desWAwcOjIaGhvjc5z4X3/3ud9t0fVOnTs0d6bC8vDx++ctf7vZyW7dujW3btrUps6ysLDZs2BDXX399rFixIqqqqmLJkiW5Iy3C3iiEmYADyUxAvkKZiQsuuCC++93vRkNDQ6xatSp+/etfx3//93+3eX3YqRBm4rOf/WysWrUqli9fHsuXL4+HHnooevbsGcuXL8+9M08+xfst1NfXx9q1a/MOtX/UUUfF7NmzY9myZXHllVfGnDlz4vOf/3xuj9MjjzwSDz74YFx55ZURETFhwoQYMmRIXHnllfHRj340xo4dGyUlJdGzZ8+8vVo7tba2RnNzc7S0tETEnvdQ7dxTttPll18e3/jGN9p0u4qKimL27Nnx29/+Nj7ykY/E7373u5g/f/4ezyMIOxXqTMC+MhOQr5Bn4tJLL413v/vdcdxxx0V5eXmMGjUqPvShD+3dHUSHU6gzccQRR0RZWVnu633ve1907tw5ysrKDthpygpOxh5NmjQp69WrV3bbbbdlWZZlS5cuzfr3758NHjw4+8c//pG73J133pmVlpZm9fX12eLFi7OpU6dmWZZl9913X3bsscdm69evz7IsyzZt2pSde+65WV1dXVZVVZV16dIlu/XWW/Ou8+Mf/3hWVVWV9ejRI+vdu3dWWlq6y1fv3r2zvn37/mfuBHgDMwH5zATkK/SZ2L59e/b4449nTz311H5n0TEU+kzQdkVZlmXtXf4PFjt27Iiampr48Ic/vMvPXnnllejVq1fesizLYsOGDbv9+59XX301mpqa8o5qCAcbMwH5zATkMxOQz0x0XIo3AAAAJORvvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAEmrzSdaKioqSbURZWVmS3Dlz5iTJjYjo379/suyDUX19fbLsAQMGJMtevXr1Pq+bciYORrW1tcmyUx6tM9Vjd8GCBUlyIyKGDRuWLHt/jrd5sM7EWWedlSx7zJgxSXJTPW9GpH3unD59erLslPZ1Lg7GmUj52Jo8eXKy7JS/F1PdJ3V1dUlyU+uIzxMppXqdm/J3ecrsysrKZNkptWUuvOMNAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAl1bu8NAID2dNZZZyXLHj16dLLsVAYMGJAse/r06cmyO5pU/0/Lli1LkpvaihUrkmUvWLAgSW7KWePgUV1dnSR3+fLlSXIjIiZPnpwsu5C9I4r3mDFjkuT2798/SW5ExMKFC5Nlp/oFnzI75XDX1dUly+5oysrKkmWXlpYmy07pqKOOSpLrcQsAwE4+ag4AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACXVu7w2IiFi2bFl7b8JeKykpSZY9ffr0ZNl1dXXJsnnnKysra+9N6DCqq6vbexNoo5S/zw9Gy5cvb+9NoA1KS0vbexP22sKFC5NlDxs2LFn2nDlzkuSm/N3j9d6BlfK1earHQcqZYN94xxsAAAASUrwBAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAICHFGwAAABJSvAEAACAhxRsAAAASUrwBAAAgIcUbAAAAElK8AQAAIKHO7b0BERFz585NknvDDTckyY2IuP7665NlV1ZWJsuePHlysmze+fz/72rhwoVJchcsWJAklwOvtra2vTfhHWX58uXtvQm0QZ8+fZLkrlixIkluRMStt96aLLuuri5Z9pw5c5LknnnmmUlyI9K9tn4nO+uss5JlX3XVVcmyP/axjyXLTiXlfZ3yObm9n++94w0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACXVu7w0AgPY0Z86cZNmVlZVJcktLS5PkRkSUlZUlyy4pKUmWXVdXlyz7nSjV4zbVYzYiYu7cucmyD0af+cxnkmV3xPt68uTJybIXLlyYLHvAgAFJcqurq5PkRqR9DrrvvvuSZY8ZMyZZdlsUdPFOOYApX5hcf/31ybJT3idwMEr5xAQAABE+ag4AAABJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACXVu7w0AgPZUW1ubLLu0tDRZdipDhw5Nll1XV5csu6NJ9bhNOQ9lZWXJslNu97Bhw5LkzpkzJ0luR1VSUpIsu3///smyU83F8uXLk+RGpH1uq66uTpbd3hTvfeTFAwejlC96AACA3fNRcwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABIqHN7b0BKAwYMSJZ91llnJcteuHBhsmw6tuXLlyfLLi0tTZZdX1+fLHvBggXJsjk4lJWVtfcmvKN4DurYUj5PVFdXJ8tO6cwzz0ySO3ny5CS5HdWwYcPaexP2SW1tbZLcOXPmJMmNiFixYkWy7EJ+XeYdbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhBRvAAAASEjxBgAAgIQUbwAAAEhI8QYAAICEFG8AAABISPEGAACAhDq39wZERJSVlSXJXbZsWZLc1MaOHdvem0CBGjNmTLLs2traZNkp1dXVtfcm0M5SPgZmzJiRJLekpCRJbkTa3xO881VWVibLrq6uTpY9evToZNmpXpctWLAgSW5HdbC+DhkwYECS3KFDhybJjUj33FbovOMNAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJCQ4g0AAAAJKd4AAACQkOINAAAACSneAAAAkJDiDQAAAAkp3gAAAJBQUZZlWXtvBAAAABQq73gDAABAQoo3AAAAJKR4AwAAQEKKNwAAACSkeAMAAEBCijcAAAAkpHgDAABAQoo3AAAAJKR4AwAAQEL/H5mbSnmaeO8ZAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化一些样本的预测结果\n",
    "n_samples = 10\n",
    "indices = np.random.choice(len(X_test), n_samples, replace=False)\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "for i, index in enumerate(indices):\n",
    "    plt.subplot(2, n_samples//2, i + 1)\n",
    "    plt.imshow(X_test[index].reshape(8, 8), cmap='gray', interpolation='none')\n",
    "    plt.title(f'真实值: {y_test[index]}\\n预测值: {y_pred[index]}', fontsize=10)\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dc1a654",
   "metadata": {},
   "source": [
    "调整n_estimators参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "0bf0177b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9465\n"
     ]
    }
   ],
   "source": [
    "rf_classifier_1 = RandomForestClassifier(n_estimators=10, random_state=1017)\n",
    "\n",
    "rf_classifier_1.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_classifier_1.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "b58fb885",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9660714285714286\n"
     ]
    }
   ],
   "source": [
    "rf_classifier_2 = RandomForestClassifier(n_estimators=50, random_state=1017)\n",
    "\n",
    "rf_classifier_2.fit(X_train, y_train)\n",
    "\n",
    "y_pred = rf_classifier_2.predict(X_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc3e8568",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}