MLS-C01 Exploratory Data Analysis Practice Question

Correct answer & explanation

Applying SMOTE before splitting the data causes data leakage. SMOTE generates synthetic samples based on the entire dataset, including the test set, so synthetic versions of test samples can appear in the training set. This inflates training accuracy artificially but does not improve the model's ability to generalize to unseen data, leading to poor recall on the true test set. Option B is incorrect because while overfitting due to lack of regularization can cause poor generalization, the specific pattern of high accuracy but very low recall is characteristic of data leakage from preprocessing before splitting. Option C is incorrect because accuracy is indeed a poor metric for imbalanced data, but the low recall (5%) indicates a fundamental issue with the model's ability to detect positives, which goes beyond metric choice. Option D is incorrect because logistic regression can be effective for imbalanced datasets when properly handled (e.g., with class weights or resampling); the problem here stems from the improper application of SMOTE, not the algorithm itself.