我有一個數據集，其中響應值不平衡，我有更多合格的拒絕值與非拒絕值，因此我希望平衡我的數據集。為此，有一個代碼可以使用現已棄用的代碼cross_validation.StratifiedKFold，但現在我需要對其進行調整，但我并不完全理解它，所以我正在尋求幫助。原來的代碼是：def stratified_cv(X, y, clf_class, shuffle=True, n_folds=10, **kwargs): stratified_k_fold = cross_validation.StratifiedKFold(y, n_folds=n_folds, shuffle=shuffle) y_pred = y.copy() # ii -> train # jj -> test indices for ii, jj in stratified_k_fold: X_train, X_test = X[ii], X[jj] y_train = y[ii] clf = clf_class(**kwargs) clf.fit(X_train,y_train) y_pred[jj] = clf.predict(X_test) return y_pred數據集 fit_transformed在哪里X，轉換為 numpy float 數組并縮放，y“拒絕”與“未拒絕”分類轉換為 int 數組（當然是 0 或 1）。最后clf_class(**kwargs)可以是分類器，例如ensemble.GradientBoostingClassifier,svm.SVC和ensemble.RandomForestClassifiery = np.array([0,0,0,0,0,1,1])
1 回答
Qyouu
TA貢獻1786條經驗 獲得超11個贊
StratifiedKFold已搬進model_selection. 所以你應該這樣做：
from sklearn.model_selection import StratifiedKFold
def stratified_cv(X, y, clf_class, shuffle=True, n_folds=10, **kwargs):
stratified_k_fold = StratifiedKFold(n_splits=n_folds, shuffle=shuffle)
y_pred = y.copy()
# ii -> train
# jj -> test indices
for ii, jj in stratified_k_fold.split(X,y):
X_train, X_test = X[ii], X[jj]
y_train = y[ii]
clf = clf_class(**kwargs)
clf.fit(X_train,y_train)
y_pred[jj] = clf.predict(X_test)
return y_pred
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