KNN算法对鸢尾花进行分类：添加网格搜索和交叉验证

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 #KNN算法对鸢尾花进行分类：添加网格搜索和交叉验证 #1、获取数据 iris = load_iris() #2、数据集划分 x_train,x_test,y_train,y_test = train_test_split(iris.data,iris.target,random_state = 22) #3、特征工程——标准化 transfer = StandardScaler() x_train = transfer.fit_transform(x_train) x_test = transfer.transform(x_test) #4、KNN算法预估器 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) estimator.fit(x_train,y_train) #5、模型评估 #方法一：直接比对真实值和预测值 y_predict = estimator.predict(x_test) print("y_predict：\n",y_predict) print("对真实值和预测值：\n",y_test == y_predict) #方法二：计算准确率 score = estimator.score(x_test,y_test) print("准确值为：\n",score) #最佳参数best_params_ print("最佳参数：\n",estimator.best_params_) #最佳结果best_score_ print("最佳结果：\n",estimator.best_score_) #最佳估计量best_estimator_ print("最佳估计量：\n",estimator.best_estimator_) #交叉验证结果 print("交叉验证结果：\n",estimator.cv_results_)