Expand description
Feature selection transformers.
This module provides three feature selection strategies:
VarianceThreshold— remove features whose variance falls below a configurable threshold (default 0.0 removes zero-variance features).SelectKBest— keep the K features with the highest ANOVA F-scores computed against a class label vector.SelectFromModel— keep features whose importance weight (provided by a previously fitted model) exceeds a configurable threshold.
All three implement the standard ferrolearn Fit / Transform pattern
and integrate with the dynamic ferrolearn_core::pipeline::Pipeline.
Structs§
- Fitted
SelectK Best - A fitted K-best selector holding per-feature scores and selected indices.
- Fitted
Variance Threshold - A fitted variance-threshold selector holding the selected column indices and the per-column variances observed during fitting.
- Select
From Model - A feature selector driven by external feature-importance weights.
- SelectK
Best - An unfitted K-best feature selector.
- Variance
Threshold - An unfitted variance-threshold feature selector.
Enums§
- Score
Func - Scoring function variants for
SelectKBest.