我下面有一個非?；镜哪_本來演示該問題：from imblearn.over_sampling import ADASYNimport pandas as pd, numpy as npfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.model_selection import train_test_splitdata = pd.read_csv('glass.csv')classes = data.values[:, -1]data = data.iloc[:, :-1]adasyn = ADASYN(sampling_strategy='not majority', random_state=8, n_neighbors=3)new_data, new_classes = adasyn.fit_resample(data, classes)X_train, X_test, y_train, y_test = train_test_split(new_data, new_classes, test_size = 0.20)rfc = RandomForestClassifier()rfc.fit(X_train, y_train)print("Score: {}".format(rfc.score(X_test, y_test)))目的是平衡以下類別的不平衡：(214,?10) Class=1,?Count=70,?Percentage=32.710% Class=2,?Count=76,?Percentage=35.514% Class=3,?Count=17,?Percentage=7.944% Class=5,?Count=13,?Percentage=6.075% Class=6,?Count=9,?Percentage=4.206% Class=7,?Count=29,?Percentage=13.551%擁有相等（或接近相等）的樣本。然而，運(yùn)行上面的代碼會產(chǎn)生：ValueError: No samples will be generated with the provided ratio settings.更改為成功地對類 進(jìn)行過采樣ADASYN，并將其帶入樣本，但仍然使其余類不平衡。因此，我正在尋找一種使用 ADASYN對所有少數(shù)類別進(jìn)行完全過采樣的方法。sampling_strategyminorityminority674ADASYN 文檔指出：?'not majority': resample all classes but the majority class;但這顯然沒有發(fā)生。