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