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SMOTE樣本生成策略

import pandas as pd from imblearn.over_sampling import SMOTE from sklearn.ensemble import RandomForestClassifier from sklearn.matrics import confusion_matrix from sklearn.model_selection import train_test_splitcredit_cards = pd.read_csv('creditcard.csv')columns = credit_cards.columns features_columns = columns.delete(len(columns)-1)features=credit_cards[features_columns] lables=credit_cards['Class']features_train, features_test, lables_train, lables_test = train_test_split(features,lables,test_size=02,random_state=0)oversampler=SMOTE(random_state=0) os_features,os_labels = oversampler.fit_sample(features_train,lables_train)print(len(os_labels[os_labels==1]))os_features = pd.DataFrame(os_features) os_labels = pd.DataFrame(os_labels) best_c = printing_Kfold_scores(os_features,os_labels)lr = LogisticRegression(C = best_c, penalty = '11') lr.fit(os_features,os_labels.values.ravel()) y_pred = lr.predict(features_test.values)cnf_matrix = confusion_matrix(lables_test,y_pred) print(np.set_printoption(precision=2))print('Recall metric in the testing dataset:',cnf_matrix[1,1]/(cnf_matrix[1,0]+cnf_matrix[1,1]))class_names = [0,1] plt.figure() plot_confusion_matrix(cnf_matrix,classes=class_names,title='Confusion matrix')

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