White Box Models and Basis Explainability
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White Box Models and Basis Explainability
You will gain skills in understanding how transparency is built into these models and how to interpret their outputs.
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- 0 Rating
- 0 Reviews
- 0 Students Enrolled
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 provides a recap of inherently interpretable, or "white box," models such as linear regression, logistic regression, decision trees, and rule-based learners. We will revisit how these models embed transparency into their structure, making it possible to directly trace how inputs lead to outputs. Participants will learn how to read and interpret model parameters, visualize decision paths, and evaluate the trade-off between interpretability and predictive performance. By the end of the session, you will have a clear understanding of how these models communicate their decision logic and why they remain essential building blocks in explainable AI.
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Meet the instructors !
Courses in ML applied to finance; network theory; eXplainable AI in finance