The approach

You've decided to first fit a base model with all the features and assess it at different levels of complexity to understand how having more features increases the propensity to overfit. Then, you employ a series of feature selection methods ranging from simple filter-based methods to the most advanced ones to determine which one achieves the profitability and reliability goals sought after by the client. Lastly, once a list of final features has been selected, at this stage, feature engineering can be considered to enhance model interpretability.

Given the cost-sensitive nature of the problem, thresholds are important to optimize the profit lift. We will get into the role of thresholds later on, but one significant effect is that even though this is a classification problem, it is best to use regression models, and then use predictions to classify so that there's only one threshold to tune. That is, for classification models, you would need a threshold for...