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  • 数据分类器

    自己实现

    import numpy as np
    
    
    def train_test_split(X, y, test_ratio=0.2, seed=None):
        """将数据 X 和 y 按照test_ratio分割成X_train, X_test, y_train, y_test"""
        assert X.shape[0] == y.shape[0], 
            "the size of X must be equal to the size of y"
        assert 0.0 <= test_ratio <= 1.0, 
            "test_ration must be valid"
    
        if seed:
            np.random.seed(seed)
    
        shuffled_indexes = np.random.permutation(len(X))
    
        test_size = int(len(X) * test_ratio)
        test_indexes = shuffled_indexes[:test_size]
        train_indexes = shuffled_indexes[test_size:]
    
        X_train = X[train_indexes]
        y_train = y[train_indexes]
    
        X_test = X[test_indexes]
        y_test = y[test_indexes]
    
        return X_train, X_test, y_train, y_test

    sklearn带的分类器‘

    from sklearn.model_selection import train_test_split
    X_train,X_test,y_train,y_test = train_test_split(x,y,test_size= 0.2,random_state=666)

    利用KNN算法测试

    from sklearn.neighbors import KNeighborsClassifier
    KNN_classifier = KNeighborsClassifier(n_neighbors=3)
    KNN_classifier.fit(X_train,y_train)
    Y_predict = KNN_classifier.predict(X_test)
    Y_predict

    判断准确率

    sum(Y_predict==y_test)/len(y_test)
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  • 原文地址:https://www.cnblogs.com/Erick-L/p/9008347.html
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