Go beyond default settings with robust tuning, cross-validation, and performance metrics for better performing machine learning models.
Go beyond grid search to tune complex machine learning models with sequential model-based optimization.
Cut training costs and accelerate model tuning with advanced search strategies.
Build dynamic, efficient, and framework-agnostic machine learning pipelines using the Define-by-Run API.
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Understand SHAP values in depth to explain machine learning models accurately, critically and with confidence.
Master global and local explainability for decision trees, random forests, and gradient boosting machines.
Explain what drives your model and each prediction using regression coefficients, odds ratios and feature importance
Interpret any model with visual tools like permutation feature importance, partial dependence plots, accumulated local effects and individual conditional expectation
Uncover the discrepancy between manufacturer energy models and real-world thermal behavior to confidently select Low-E glazing that actually performs in high-heat conditions.
Upgrade traditional PPC systems with AI-driven decision-making to master modern manufacturing efficiency.
