Explainable AI for Tree-Based Models: From SHAP to PDP and Feature Interactions

Описание: In this AI Tech Experts Webinar, Jakub Cieślak, Senior Data Scientist, shows how to interpret tree-based models using practical XAI techniques and how to avoid the most common traps.

The talk focuses on tabular ML problems and walks step by step through:

👉 local vs global explanations and when to use each,
👉 SHAP values and breakdown plots for individual predictions,
👉 partial dependence plots (PDP) and ICE for global feature effects,
👉 feature importance vs causality and why this distinction matters,
👉 detecting data leakage, hidden bias and misleading patterns,
👉 common XAI mistakes and concrete recommendations.

Examples are illustrated on a classic Titanic dataset, but the lessons apply directly to real-world decision systems used by product teams, stakeholders and regulated industries.

00:30 Introduction to explainable AI
03:33 Explaining predictions with SHAP
06:27 Why explain predictions: quality and trust
09:50 Legal and ethical requirements for XAI
11:31 Common mistakes and best practices

Check our website: https://deepsense.ai/
Linkedin:   / applied-ai-insider  

#ExplainableAI #XAI #TreeBasedModels #ModelInterpretability #MachineLearning #DataScience #FeatureImportance #SHAP #PartialDependence #MLTransparency