Research pilot applications are open. Apply for pilot →
Features

Feature explanations

available

Use SHAP-based feature importance to inspect which variables most strongly contribute to supervised or labeling results.

Overview

Feature explanations help identify which variables contribute most strongly to the analytical result.

Ounias uses SHAP-based explanations and keeps the explanation artifacts attached to the run that produced them.

Available outputs

  • Feature importance
  • Run-linked SHAP artifacts
  • Feature-level contribution information
  • Comparison with other analytical outputs
  • Exportable explanation tables

Interpretation

  • Feature importance describes model behavior rather than causal influence.
  • Correlated variables can complicate attribution.
  • Explanation quality depends on the model, data representation, and analytical configuration.