Formula Used
Absolute ranking handles signed coefficients consistently. Percentage contribution uses total absolute importance. These formulas support clear cross-feature comparisons for practical model review.
How to Use
- Select the importance method and model family.
- Enter feature names with primary importance scores.
- Use pipes to average repeated runs automatically.
- Add comparison scores to measure stability and rank changes.
- Choose thresholds, cumulative limits, weights, and grouping.
- Calculate, review diagnostics, then export selected features.
Example Data
| Feature | Primary runs | Comparison runs | Group |
|---|---|---|---|
| Age | 0.18|0.21|0.19 | 0.17|0.18|0.16 | Demographics |
| Income | 0.31|0.29|0.33 | 0.28|0.30|0.29 | Financial |
| Tenure | 0.16|0.14|0.15 | 0.18|0.17|0.16 | Engagement |
| Support Calls | -0.11|-0.12|-0.10 | -0.08|-0.09|-0.07 | Engagement |
| Usage Frequency | 0.24|0.26|0.25 | 0.22|0.23|0.21 | Engagement |
Method Comparison Guide
| Method | Best use | Main limitation |
|---|---|---|
| Coefficients | Linear and logistic models | Scaling affects magnitude |
| Tree impurity | Fast internal tree ranking | High-cardinality bias |
| Permutation | Model-agnostic validation | Correlated features share impact |
| SHAP | Local and global explanations | Higher computational cost |
| Mutual information | Nonlinear target relationships | Estimator sensitivity |
Common Mistakes
Do not treat importance as causal evidence. Avoid comparing unscaled coefficients without preparation. Validate important features using alternative methods and repeated evaluations.
Correlated predictors can divide importance unpredictably. Leakage can create unrealistically dominant variables. Review data collection timing before trusting final rankings.
Frequently Asked Questions
What does feature importance measure?
It measures how strongly a model relies on predictors. Different methods define reliance differently. Always interpret scores within the selected model and method.
Should negative scores be removed?
Negative coefficients can contain useful directional information. Their absolute values still show influence strength. Remove them only when your method defines negatives invalid.
Why use absolute importance?
Absolute values rank positive and negative effects together. This helps measure influence magnitude consistently. Signed values remain available for directional interpretation.
What is cumulative importance?
Cumulative importance adds contributions from highest-ranked features. It supports compact feature subset selection. Common cutoffs include eighty, ninety, or ninety-five percent.
Why compare two score sets?
Comparisons reveal instability across folds, datasets, or models. Large rank shifts need investigation. Stable features usually support more dependable model interpretation.
How are repeated runs handled?
Separate run scores with pipe characters. The calculator averages those values automatically. It also reports population standard deviation for each feature.
Can correlated features distort rankings?
Yes, correlated predictors can split or exchange importance. Enter known correlation pairs for warnings. Consider grouped analysis, permutation importance, or SHAP checks.
Does high importance prove causation?
No, importance only describes model dependence. Confounding and leakage can produce misleading rankings. Causal claims require stronger study designs and assumptions.
When should zero-importance features be removed?
Remove them after confirming evaluation stability. Retrain the model after feature removal. Compare predictive performance before accepting the reduced feature set.