Handling Missing Features

Understanding how to represent missing features is crucial for model performance. By introducing indicator variables, models can learn to predict effectively even when certain data is absent. Additionally, employing multiple models allows for predictions based on varying degrees of data availability, ensuring robust performance even with limited information.