A healthcare organization deploys a predictive model to forecast patient readmissions. Over several months, they notice that due to evolving treatment protocols and patient demographics, the model’s accuracy declines. In response, the team collects new data, revisits data preprocessing, rebuilds the model, and re-deploys it. Which phase of the data mining lifecycle is most emphasized by this ongoing process?

Why is feature selection considered a critical step in the machine learning pipeline?