Before feeding variables into the RWN, the features must be uniform to prevent the weights from being biased by large-magnitude variables.

: If using an automated search, treat each feature as a categorical parameter (True/False) and optimize for the highest F1 score. 5. Validation Cross-Validation : Use a

column vector to identify which initial choices have the strongest correlation with the target.

: Apply a normalization formula (e.g., Eq. 14 in standard FS protocols) to ensure weights are comparable across different nodes or decision trees. 4. Selection via Subset Optimization

Rwn - Choices [fs004] (2026)

Before feeding variables into the RWN, the features must be uniform to prevent the weights from being biased by large-magnitude variables.

: If using an automated search, treat each feature as a categorical parameter (True/False) and optimize for the highest F1 score. 5. Validation Cross-Validation : Use a RWN - Choices [FS004]

column vector to identify which initial choices have the strongest correlation with the target. Before feeding variables into the RWN, the features

: Apply a normalization formula (e.g., Eq. 14 in standard FS protocols) to ensure weights are comparable across different nodes or decision trees. 4. Selection via Subset Optimization Before feeding variables into the RWN