39. CART (Classification and Regression Tree).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "indie-sarah",
   "metadata": {},
   "source": [
    "# CART 二叉树\n",
    "\n",
    "## Classification\n",
    "\n",
    "###### 常用参数\n",
    "\n",
    "+ 参数的功能：防止模型过拟合或欠拟合，降低训练模型的时间复杂度；\n",
    "    1. max_depth = 2：决策树模型的最高深度为 2；\n",
    "    2. criterion = 'entropy'：划分节点数据集时，采用信息熵为判断条件；\n",
    "    3. criterion = 'gini'：划分节点数据集时，采用基尼系数为判断条件；\n",
    "    4. min_samples_split = 10：节点数据集中至少要有 10 个样本，才能继续对节点数据集进行划分；否则即使信息熵和基尼系数都比较大，也不在划分数据集；\n",
    "    5. min_samples_leaf = 6：一个“叶子”节点，至少要有 6 个样本,一个节点在分枝后的每个子节点都必须包含至少min_samples_leaf个训练样本，否则分枝就不会发生；\n",
    "    6. max_leaf_nodes = 4：模型最多只能有 4 个“叶子”节点；\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "electronic-sacrifice",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "perceived-fence",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets \n",
    "\n",
    "X, y = datasets.make_moons(noise = 0.25, random_state = 666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "severe-egypt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[y ==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y ==1, 0], X[y==1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "insured-airport",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt_clf = DecisionTreeClassifier()\n",
    "dt_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "electric-chart",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_decision_boundary(model, axis):\n",
    "    \n",
    "    x0, x1 = np.meshgrid(\n",
    "        np.linspace(axis[0], axis[1], int((axis[1]-axis[0])*100)).reshape(-1,1),\n",
    "        np.linspace(axis[2], axis[3], int((axis[3]-axis[2])*100)).reshape(-1,1)\n",
    "    )\n",
    "    X_new = np.c_[x0.ravel(), x1.ravel()]\n",
    "    \n",
    "    y_predict = model.predict(X_new)\n",
    "    zz = y_predict.reshape(x0.shape)\n",
    "    \n",
    "    from matplotlib.colors import ListedColormap\n",
    "    custom_cmap = ListedColormap(['#EF9A9A','#FFF59D','#90CAF9'])\n",
    "    \n",
    "    plt.contourf(x0, x1, zz, linewidth=5, cmap=custom_cmap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "otherwise-branch",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yasmine/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.scatter(X[y==2, 0], X[y==2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "working-technology",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_depth=2)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf2 = DecisionTreeClassifier(max_depth = 2)\n",
    "dt_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "characteristic-latest",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yasmine/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf2, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.scatter(X[y==2, 0], X[y==2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fitting-necessity",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(min_samples_split=10)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf3 = DecisionTreeClassifier(min_samples_split = 10)\n",
    "dt_clf3.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "primary-attribute",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yasmine/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf3, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.scatter(X[y==2, 0], X[y==2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "built-shame",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(min_samples_leaf=6)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf4 = DecisionTreeClassifier(min_samples_leaf = 6)\n",
    "dt_clf4.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fifteen-variable",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yasmine/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf4, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.scatter(X[y==2, 0], X[y==2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "nuclear-warehouse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(max_leaf_nodes=4)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf5 = DecisionTreeClassifier(max_leaf_nodes = 4)\n",
    "dt_clf5.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "presidential-turkey",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/yasmine/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:15: UserWarning: The following kwargs were not used by contour: 'linewidth'\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_decision_boundary(dt_clf5, axis=[-1.5, 2.5, -1.0, 1.5])\n",
    "plt.scatter(X[y==0, 0], X[y==0, 1])\n",
    "plt.scatter(X[y==1, 0], X[y==1, 1])\n",
    "plt.scatter(X[y==2, 0], X[y==2, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "classified-saint",
   "metadata": {},
   "source": [
    "## Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cutting-bidding",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "deluxe-brown",
   "metadata": {},
   "outputs": [],
   "source": [
    "boston = datasets.load_boston()\n",
    "X = boston.data\n",
    "y = boston.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "aquatic-venice",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=666)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "faced-vancouver",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "dt_reg = DecisionTreeRegressor()\n",
    "dt_reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "returning-pontiac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5853645290723282"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "joint-solid",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "physical-console",
   "metadata": {},
   "source": [
    "#### Adjust Parameter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "cardiac-posting",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "def plot_learning_curve(algo, X_train, X_test, y_train, y_test):\n",
    "    train_score = []\n",
    "    test_score = []\n",
    "    for i in range(1, len(X_train)+1):\n",
    "        algo.fit(X_train[:i], y_train[:i])\n",
    "        \n",
    "        y_train_predict = algo.predict(X_train[:i])\n",
    "        train_score.append(mean_squared_error(y_train[:i], y_train_predict))\n",
    "    \n",
    "        y_test_predict = algo.predict(X_test)\n",
    "        test_score.append(mean_squared_error(y_test, y_test_predict))\n",
    "    \n",
    "    plt.plot([i for i in range(1, len(X_train)+1)],\n",
    "            np.sqrt(train_score), label=\"train\")\n",
    "    plt.plot([i for i in range(1, len(X_train)+1)],\n",
    "            np.sqrt(test_score), label=\"test\")\n",
    "    \n",
    "    plt.legend()\n",
    "    plt.axis([0, len(X_train)+1, 0, 4])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "interracial-karma",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(dt_reg, X_train, X_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "decent-representation",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(min_samples_leaf=2)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_reg2 = DecisionTreeRegressor(min_samples_leaf = 2)\n",
    "dt_reg2.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "operational-dodge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_learning_curve(dt_reg2, X_train, X_test, y_train, y_test)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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