Feature Importance
This method helps you understand which features matter most for predicting a target variable. The engine trains several models, compares their performance, then explains the best model using SHAP to produce feature level importance.
What the engine does
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Firstly, the engine trains a set of tree-based models, namely:
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Decision Tree
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Random Forest
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Extra Trees
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LightGBM
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Then, we evaluate each model using 10 fold cross validation and rank them by mean accuracy
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The system keeps the best performing model and performs SHAP process generating feature importance results
How to run (UI)
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Open the link for the Feature Importance along with dataset-id parameter (e.g.,
https://data-valuation-ui.aidapt.s5projects.eu/feature-importance?dataset-id=XXX) -
Select the target metric
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Start the analysis and expect a short wait due to cross validation
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Review the outputs
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Model leaderboard with the ranked models and their scores
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SHAP results showing feature importance
Figure 1. Feature Importance results in the UI
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Optionally download the results as CSV for further analysis
Interpretation
The model leaderboard helps you compare model quality under the same evaluation setup. A higher mean accuracy indicates better performance on average across folds.
SHAP values explain how features influence the model prediction.
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Larger absolute SHAP values indicate more influential features
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Positive SHAP values generally push predictions toward the positive class while negative values push them away, the exact meaning depends on how the target is encoded
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If two features are strongly correlated, importance may be shared between them so each one can appear less dominant on its own