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  • 随机森林分类器学习

    转自:https://blog.csdn.net/gracejpw/article/details/102593225

    1.sklearn建立随机森林分类器

    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_wine
    from sklearn.model_selection import train_test_split
    wine = load_wine()
    wine
    wine.data
    wine.target
    #切分训练集和测试集
    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)
    #查看模型效果
    score_c = clf.score(Xtest,Ytest)
    score_r = rfc.score(Xtest,Ytest)
    #打印最后结果
    print("Single Tree:",score_c)
    print("Random Forest:",score_r)

    Single Tree: 0.8888888888888888
    Random Forest: 0.9444444444444444

    2.红酒数据集

     它包含11个特征,以及quality分数,从0至9表示10个级别,随机森林可以将它们成功地多分类。

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  • 原文地址:https://www.cnblogs.com/BlueBlueSea/p/14881320.html
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