Basic Explainability – Feature Importance, PDP, ICE
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Basic Explainability – Feature Importance, PDP, ICE
Covers foundational explainability techniques including feature importance, partial dependence plots (PDP), and individual conditional expectation (ICE). You will learn to evaluate model behavior and interpret the influence of features.
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Courselet Content
Requirements
- 1) Foundations in Machine Learning – understanding of supervised learning, model training, overfitting, feature importance. 2) Mathematical Background – basic linear algebra, probability, and statistics (enough to follow Shapley values and regression concepts). 3) Programming Skills – ability to work with Python (libraries such as scikit-learn, XGBoost, SHAP, LIME).
General Overview
Description
This session introduces core explainability techniques that help uncover how machine learning models make predictions. We begin with feature importance, a global method that ranks variables by their overall contribution to model performance. From there, we move to partial dependence plots (PDPs), which visualize the average effect of one or two features on the predicted outcome, and individual conditional expectation (ICE) plots, which reveal how these effects vary across individual observations. By combining these approaches, you will gain both a global and local perspective on model behavior, learn how to interpret feature influence in practical settings, and recognize the limitations of these methods in capturing complex interactions.
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Meet the instructors !
Courses in ML applied to finance; network theory; eXplainable AI in finance