Rwn - Choices [fs004] Guide

: Rank features by their FIM or SHAP values. Thresholding : Select the top features (or those exceeding a specific threshold ) to obtain the target subset.

: Replace null values with the mean/median for continuous data or the mode for categorical data. Normalization : Scale all features to a range of using Min-Max scaling or Z-score standardization. 2. Disambiguated Training Set Preparation

To prepare the "Choices" feature for the or related feature selection systems (often designated by codes like FS004 ), follow these procedural steps to ensure the data is optimized for the selection algorithm. 1. Data Sanitization and Scaling RWN - Choices [FS004]

For partial label learning or complex selection tasks (as specified in [FS004] workflows), derive a disambiguated set.

Once importance is calculated, reduce the "Choices" set to the most impactful variables. : Rank features by their FIM or SHAP values

-fold cross-validation approach to ensure the "Choices" selected are robust and not overfitted to a specific training slice.

The "Choices" feature is often refined by calculating the . Column Vector Calculation : Calculate the Normalization : Scale all features to a range

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