Exercises

1. Explore the various undersampling APIs available from the imbalanced-learn library at https://imbalanced-learn.org/stable/references/under_sampling.html.
2. Explore the NearMiss undersampling technique, available through the imblearn.under_sampling.NearMiss API. Which class of methods does it belong to? Apply the NearMiss method to the dataset that we used in the chapter.
3. Try all the undersampling methods discussed in this chapter on the us_crime dataset from UCI. You can find this dataset in the fetch_datasets API of the imbalanced-learn library. Find the undersampling method with the highest f1-score metric for LogisticRegression and XGBoost models.
4. Can you identify an undersampling method of your own? (Hint: think about combining the various approaches to undersampling in new ways.)