Exercises

1. Explore the two variants of SMOTE, namely KMeans-SMOTE and SVM-SMOTE, from the imbalanced-learn library, not discussed in this chapter. Compare their performance with vanilla SMOTE, Borderline-SMOTE, and ADASYN using the logistic regression and random forest models.
2. For a classification problem with two classes, let’s say the minority class to majority class ratio is 1:20. How should we balance this dataset? Should we apply the balancing technique at test or evaluation time? Please provide a reason for your answer.
3. Let’s say we are trying to build a model that can estimate whether a person can be granted a bank loan or not. Out of the 5,000 observations we have, only 500 people got the loan approved. To balance the dataset, we duplicate the approved people data and then split it into train, test, and validation datasets. Are there any issues with using this approach?
4. Data normalization helps in dealing with data imbalance. Is this true? Why...