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@busekoseoglu I can't reproduce this problem with my synthetic data for testing. Could you please share an example dataset to reproduce this problem?
BTW, you don't have to use one hot encoding before AutoML.fit(). It often works better without this encoding.
Code:
categorical_transformer = Pipeline(steps=[('one_hot', OneHotEncoder())])
categorical_features = ['merchant_category', 'merchant_group',"name_in_email"]
preprocessor = ColumnTransformer(
transformers=[
('cat', categorical_transformer, categorical_features)
])
clf = Pipeline(steps=[('missing', fill_missing()),
('outlier', outlier_filling()),
('preprocessor', preprocessor),
('classifier', AutoML())])
clf.fit(X_train, y_train)
Note: It works when RandomForestClassifier is replaced with AutoML.
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