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  • scikit-learn中自带的均值方差归一化函数

    一:所在包

        from sklearn.preprocessing import StandardScaler。

    二:步骤

      a.将训练集进行fit操作

      b.在将训练集进行transform操作,得到均值为0,方差为1的数据集。

      c.对测试集进行transform操作,但是不需要在进行fit,应使用训练集fit后得出的参数。

    三:代码

    import numpy as np
    from sklearn import datasets
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.preprocessing import StandardScaler
    from sklearn.model_selection import train_test_split
    
    iris = datasets.load_iris()
    x = iris.data
    y = iris.target
    
    x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.2,random_state=666)
    
    standard = StandardScaler()
    standard.fit(x_train)
    x_train = standard.transform(x_train)
    
    x_test_standard = standard.transform(x_test)
    
    knn = KNeighborsClassifier(n_neighbors=3,n_jobs=-1)
    
    knn.fit(x_train,y_train)
    
    
    score = knn.score(x_test_standard,y_test)
    
    print(score)
    

      

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  • 原文地址:https://www.cnblogs.com/lyr999736/p/10682682.html
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