Beyond Regression: Harnessing Explainable AI for Machine Learning Predictions

Participants will have an understanding of predictive modeling beyond traditional regression, how to implement and evaluate supervised machine learning models like Random Forest, SVM, XGBoost and logistic regression for classification tasks. They will understand Model Evaluation Techniques like confusion matrices for classification performance and other performance Metrics. They will gain insight into Feature Importance, like identifying which variables most influence the predictions. They will also gain insight into Explainable AI (XAI) with SHAP and DALEX : By Using SHAP values to understand individual predictions, Visualizing feature effects with DALEX to interpret model decisions. The participants will experience hands-on experience with R packages like DALEX, SHAP, caret, and others and have the ability to replicate the workflow on their own datasets.