Building a rainfall prediction model with thousands of features; you want to reduce feature count to speed training with minimal accuracy loss. Which action is appropriate?

Combining highly co-dependent (redundant) features into a single representative feature (e.g., through Principal Component Analysis or feature engineering) is an effective dimensionality reduction technique. It reduces the number of features, speeding up training, while preserving most of the original information, thus minimizing accuracy loss. Removing features highly correlated with output labels would discard valuable predictive information, severely impacting model accuracy. Averaging features in fixed groups without considering their relationships is arbitrary and unlikely to preserve meaningful information or improve performance. Dropping features with over 50% null values is a valid data cleaning step, but it's a separate concern from reducing dimensionality for performance and doesn't directly address redundancy among existing, non-null features.