隨機森林分類器學習
阿新 • • 發佈:2021-06-13
轉自:https://blog.csdn.net/gracejpw/article/details/102593225
1.sklearn建立隨機森林分類器
import numpy as np import pandas as pd import matplotlib.pyplot as plt %matplotlib inline from sklearn.tree import DecisionTreeClassifier from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_winefrom sklearn.model_selection import train_test_split wine = load_wine() wine wine.data wine.target #切分訓練集和測試集 Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3) #建立模型 clf = DecisionTreeClassifier(random_state=0) rfc = RandomForestClassifier(random_state=0) clf = clf.fit(Xtrain,Ytrain) rfc= rfc.fit(Xtrain,Ytrain) #檢視模型效果 score_c = clf.score(Xtest,Ytest) score_r = rfc.score(Xtest,Ytest) #列印最後結果 print("Single Tree:",score_c) print("Random Forest:",score_r)
Single Tree: 0.8888888888888888
Random Forest: 0.9444444444444444
2.紅酒資料集
它包含11個特徵,以及quality分數,從0至9表示10個級別,隨機森林可以將它們成功地多分類。