How do I apply RandomUnderSalmpling and OverSampling in StratifiedKfoldCrossValidation?

Currently undergoing a classification taks where I have to predict customer default using a dataset that is provided by LendingClub. For my fisrst model I decided to test the Logistic Regression using SGD.

I created this initial pipeline:

imputer = SimpleImputer(strategy = "median")

scaler = StandardScaler()

model = SGDClassifier(loss='log',random_state=42,n_jobs=-1,warm_start=True)

pipeline_sgdlogreg = make_pipeline(imputer, scaler, model)

Defined my strategy:

KF = StratifiedKFold(n_splits = 5)

And performed GridSearchCV:

grid_sgdlogreg = GridSearchCV(pipeline_sgdlogreg, param_grid_sgdlogreg,
                              scoring = 'roc_auc', pre_dispatch = 3,
                              n_jobs = -1, cv = KF, verbose = 5)

search =, y_train)

Due to class imbalance the model is servery lacking in both recall and precision which does make sense.

I wanted to test out different sampling strategies. Consider this undersample approach. Made nem subsamples only ont the training data:

X_train_subsample, y_train_subsample = rus.fit_resample(X_train, y_train)

Pipeline that includes randomundersampler:

pipeline_sgdlogreg_rus = Pipeline([("Rus", RandomUnderSampler(sampling_strategy = "majority", random_state = 42)),
                               ('imputer', SimpleImputer(strategy = "median")),
                               ('scaler', StandardScaler()),

Performed GridSearchCV again

grid_sgdlogreg = GridSearchCV(pipeline_sgdlogreg_rus, param_grid_sgdlogreg_rus,
                              scoring='roc_auc', pre_dispatch=3, n_jobs=-1, cv=KF, verbose=5)

search =, y_train_subsample)

What I would like to know is if I am doing this correctly?

I have already dealt with outliers and label enconding before the split and I want to make sure that I am doing this for every fold.

Do I need to split the data again or by using the RandomUnderSampler () in the pipeline this command does that automatically for every fold?

Thank You!

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