Enum SequentialFeatureSelectionDirection
Defines the direction of sequential feature selection.
public enum SequentialFeatureSelectionDirectionFields
Backward = 1Backward elimination starts with all features and iteratively removes the feature whose removal least degrades performance.
For Beginners: Backward elimination is like pruning a tree. You start with all the branches and trim away the ones that contribute least to the tree's health. You keep trimming until you reach your desired size.
Advantages: - Considers all features working together initially - Fast when you want to keep most features - Good at identifying truly redundant features
Disadvantages: - Slow to start (must train with all features initially) - Computationally expensive with many features - Once a feature is removed, it can't be added back
Forward = 0Forward selection starts with zero features and incrementally adds the feature that most improves performance.
For Beginners: Forward selection is like building a team by adding members one at a time. You start with no one, then add the person who contributes the most. Then you add another person who, combined with the first, provides the best improvement. You continue until you have your desired team size.
Advantages: - Fast when you want to select only a small number of features - Good for exploring which features work well together
Disadvantages: - Once a feature is added, it stays (no way to remove it later if it becomes redundant) - Can be slow if you want to select many features
Remarks
For Beginners: Sequential feature selection can work in two directions: starting with no features and adding them, or starting with all features and removing them.
Think of it like packing for a trip: - Forward Selection: Start with an empty suitcase and add items one by one, choosing the most important item each time until you have enough. - Backward Elimination: Start with a full suitcase and remove items one by one, removing the least important item each time until you reach your desired size.