Day_044_HW-隨機森林林 - 程式碼撰寫.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## [作業重點]\n",
    "確保你了解隨機森林模型中每個超參數的意義，並觀察調整超參數對結果的影響"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作業\n",
    "\n",
    "1. 試著調整 RandomForestClassifier(...) 中的參數，並觀察是否會改變結果？\n",
    "2. 改用其他資料集 (boston, wine)，並與回歸模型與決策樹的結果進行比較"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston, load_wine\n",
    "from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error, r2_score, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 讀取 Boston 資料\n",
    "boston = load_boston()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.1, random_state=4)\n",
    "\n",
    "# 建立一個線性回歸模型\n",
    "regr = RandomForestRegressor()\n",
    "\n",
    "# 將訓練資料丟進去模型訓練\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# 將測試資料丟進模型得到預測結果\n",
    "y_pred = regr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 9.51\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean squared error: %.2f\"\n",
    "      % mean_squared_error(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\sklearn\\ensemble\\forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "# 讀取 wine 資料\n",
    "wine = load_wine()\n",
    "\n",
    "# 切分訓練集/測試集\n",
    "x_train, x_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.1, random_state=4)\n",
    "\n",
    "# 建立一個羅吉斯回歸模型\n",
    "regr = RandomForestClassifier()\n",
    "\n",
    "# 將訓練資料丟進去模型訓練\n",
    "regr.fit(x_train, y_train)\n",
    "\n",
    "# 將測試資料丟進模型得到預測結果\n",
    "y_pred = regr.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  1.0\n"
     ]
    }
   ],
   "source": [
    "acc = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy: \", acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019/10/16"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}