sklearn实现kNN

对鸢尾花数据集进行分类并交叉验证

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV
def kNN_iris_gscv():
    """
    用kNN对鸢尾花进行分类,添加网格搜索和交叉验证
    :return:
 """
    #1.获取数据
    iris=load_iris()
    #2.划分数据集
    x_train,x_test,y_train,y_test=train_test_split(iris.data,iris.target,random_state=1)
    #3.特征工程：标准化
    transfer=StandardScaler()
    x_train=transfer.fit_transform(x_train)
    x_test=transfer.transform(x_test) #使用训练集的平均值和标准差
    #4.模型训练
    estimator=KNeighborsClassifier()
    #加入网格搜索和交叉验证
    #参数准备
    param_dict={"n_neighbors":[1,3,5,7,9,11]}
    estimator=GridSearchCV(estimator,param_grid=param_dict,cv=10) #对estimator预估器进行10折交叉验证
    estimator.fit(x_train,y_train) #模型拟合
    #5.模型评估
    #方法1：比对真实值和预测值
    y_predict=estimator.predict(x_test)
    print(y_predict)
    print("直接比对真实值和预测值:
",y_predict==y_test)
    #方法2：直接计算准确率
    score=estimator.score(x_test,y_test)
    print("准确率为:",score)

    #最佳参数:best_params
    print("最佳参数:
",estimator.best_params_)
    #最佳结果:best_score_
    print("最佳结果:
", estimator.best_score_)
    #最佳估计器:best_estimator_
    print("最佳估计器:
", estimator.best_estimator_)
    #交叉验证结果:cv_results_
    print("交叉验证结果:
", estimator.cv_results_)
    return None


if __name__=="__main__":
    kNN_iris_gscv()