KNN sklearn

from sklearn.neighbors import KNeighborsClassifier #导入KNN分类库
#sklearn自带的train_test_split
x_train,x_test,y_train,y_test=train_test_split(x,y)
knn_classifier=KNeighborsClassifier(6)#参数为K
knn_classifier.fit(x_train,y_train)
y_predict=knn_classifier.predict(x_test)
scores=knn_classifier.score(x_test,y_test)
print('acc:{}'.format(sum(y_predict==y_test)/len(x_test)),scores)
#寻找最好的K值
#def main():
    from sklearn import datasets
    digits=datasets.load_digits()
    x=digits.data
    y=digits.target
    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)
    from sklearn.neighbors import KNeighborsClassifier
    # 寻找最好的k
    best_k=-1
    best_score=0
    for i in range(1,11):
        knn_clf=KNeighborsClassifier(n_neighbors=i)
        knn_clf.fit(x_train,y_train)
        scores=knn_clf.score(x_test,y_test)
        if scores>best_score:
            best_score=scores
            best_k=i
    print('最好的k为:%d,最好的得分为:%.4f'%(best_k,best_score))
if __name__ == '__main__':
    main()