Effective models depend on high-quality, informative features. Feature evaluation helps identify which features contribute meaningfully to model performance and which may be redundant or harmful.

Objectives

  • Determine the relevance, predictive power, and redundancy of features.
  • Guide feature selection, engineering, and model interpretation.

Core Aspects

Feature Importance

Feature Relationships Assess:

  • Correlation between features to detect redundancy
  • Interactions that may impact the target variable

Performance Impact: Measure how feature inclusion/exclusion affects:

  • Model accuracy, precision, AUC, etc.
  • Stability and generalisability of results

When Evaluation is Complete

  • Feature set achieves optimal model performance
  • Further changes offer no significant improvement
  • Feature effects are interpretable and aligned with domain knowledge