Automated Machine Learning
ML Explainability
Use AutoML to understand your data.
Explain your ML models.
ML Explainability Overview
Model Description
Keep all your model information in one place. Save the hyperparameters setup, validation strategy, optimized metric and learning time.
Metric Details
Track many metrics at once. Use automated threshold search for binary classification. Check confussion matix for classification problems.
Learning Curve
Always check learning curves from your model. Avoid overfitting with early-stopping (set on by default).
Decision Tree Visualization
Visualize your Decision Trees to better understand data and ML model.
Linear Model Coefficients
Inspect your Linear Model Coefficients.
Permutation-Based Feature Importance
Compute Feature Importance for any Machine Learning model with Permutation-Based method.
SHAP-Based Feature Importance
Compute Feature Importance with SHAP values.
SHAP Dependency Plots
Discover data dependencies with SHAP values.
SHAP Decision Plots
Check which features are used when ML model makes decision.
