Exploring Tools for Interpretable Machine Learning - Juan Orduz | PyData Global 2021

Exploring Tools for Interpretable Machine Learning
Speaker: Juan Orduz

Summary

Description

Next we explore model specific ways to understand the models predictions: (1) For the linear model we explore the beta coefficients and weight effects (2) For the XGBoost regressor we explore metrics like gain and cover.Finally we move to model agnostic methods such as (1) partial dependency (PDP) and individual conditioning expectation (ICE) plots (2) permutation importance and (3) SHAP values.We will describe the pros and cons of each methods. We do not focus on the theory behind but rather use the concrete use case to highlight their strength and limitations.

Two great references on the subject are:

Interpretable Machine Learning, A Guide for Making Black Box Models Explainable by Christoph Molnar
Interpretable Machine Learning with Python by Serg Masís

Juan Orduz's Bio
Mathematician & Data Scientist.

PyData Global 2021

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.

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