文章目錄

• 效果
• 代碼

效果

代碼

from sklearn.datasets import load_iris import numpy as np#獲取數(shù)據(jù)集 dataset=load_iris() X=dataset.data Y=dataset.target#數(shù)據(jù)轉(zhuǎn)換 X=np.array(X,dtype='float') Y=np.array(Y,dtype='int')#方法1 from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier from sklearn.preprocessing import MinMaxScaler#數(shù)據(jù)歸一化 X_transformed=MinMaxScaler().fit_transform(X)#數(shù)據(jù)集分割 X_train, X_test, Y_train, Y_test=train_test_split(X_transformed, Y, random_state=14) #模型訓(xùn)練 estimator=KNeighborsClassifier() estimator.fit(X_train,Y_train)#模型測(cè)試 Y_predicted=estimator.predict(X_test) accuracy=np.mean(Y_test==Y_predicted)*100 print("方法1:The accuracy is {0:.1f}%".format(accuracy))#方法2:流水線 from sklearn.pipeline import Pipeline from sklearn.preprocessing import MinMaxScaler from sklearn.neighbors import KNeighborsClassifier from sklearn.model_selection import cross_val_score#歸一化 Scale=MinMaxScaler()#模型選擇 Predict=KNeighborsClassifier() scaling_pipeline=Pipeline([('scale',Scale),('predict',Predict)])#模型交叉驗(yàn)證 scores=cross_val_score(scaling_pipeline, X, Y, scoring="accuracy") accuracy=np.mean(scores)*100 print("方法2:The accuracy is {0:.1f}%".format(accuracy))

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