I wanna simultaneously apply cross-validation and over-sampling. I get the following error from this code:
from sklearn.pipeline import Pipeline, make_pipeline
imba_pipeline = make_pipeline(SMOTE(random_state=42),
LogisticRegression(C=3.4))
cross_val_score(imba_pipeline, X_train_tf, y_train, scoring='f1-weighted', cv=kf)
ll intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'SMOTE(k_neighbors=5, kind='deprecated', m_neighbors='deprecated', n_jobs=1, out_step='deprecated', random_state=42, ratio=None, sampling_strategy='auto', svm_estimator='deprecated')' (type ) doesn't
PS. I get the same error using imblearn.over_sampling.RandomOverSampler rather than SMOTE.
You should import make_pipeline
from imblearn.pipeline
and not from sklearn.pipeline
: make_pipeline
from sklearn needs the transformers to implement fit
and transform
methods but SMOTE
does not implement transform
.
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