Hello team. I have a question about Borderline SMOTE:
The variant 2 is supposed to interpolate between the minority in danger and other neighbors from the minority, and the minority in danger and some neighbors from the majority.
In line https://github.com/scikit-learn-contrib/imbalanced-learn/blob/4162d2d/imblearn/over_sampling/_smote.py#L352
we train a KNN only on the minority class and then derive the neighbors nns from it, which we use for the interpolation.
Then we use that nns to obtain the neighbors from the majority class in the second part (https://github.com/scikit-learn-contrib/imbalanced-learn/blob/4162d2d/imblearn/over_sampling/_smote.py#L397) of the borderline-2 code. But would not nns contain only neighbours from the minority? as it is derived from a knn trained only in the minority class?
0.13in order to generate (and add) a single minority instance. So the new ratio will be 90:11.
BalancedBaggingClassifier(that use a
RandomUnderSamplerwith a strong learner as a
TypeError: All intermediate steps of the chain should be estimators that implement fit and transform or fit_resample. 'Pipeline(steps=[('smote', SMOTE(n_jobs=-1, random_state=42)), ('under', RandomUnderSampler(random_state=42))])' implements both)Also, can someone explain what this error means? The Pipeline only exposes fit and fit_resample methods, since, transform is not being implemented, the first condition is not met and the second one about fit_resample is being met. Then, shouldn't this work? Thank you.
Main_Pipeline = imb_Pipeline([ ('feature_handler', FeatureTransformer(list(pearson_feature_vector.index))), ('smote', SMOTE()), ('random_under_sampler', RandomUnderSampler()), ('scaler', StandardScaler()), ('pca', PCA(n_components=0.99)), ('model', LogisticRegression(max_iter=1750)), ])