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Evaluation

Predictive Analytics

Level 2: General knowledge of the subject (6-9 Years)

C:MM:L2

S:DA:L2

S:BT:L1

T:AN:L2

CS-20 - Interpretability and Transparent Machine Learning

Monday, November 6, 2023
2:45 PM – 3:45 PM PST
Location: Palos Verdes (1st Floor)
This session will discuss interpretation techniques for machine learning and AI. The session will focus on making machine learning models and their decisions interpretable. We will explore and compare different interpretation techniques (Feature Importance, Accumulated Local Effects, Partial Dependency Plots, Shapley Values, etc.). How do these techniques work? What are the advantages and disadvantages for each technique? How can their outputs be interpreted?

Learning Objectives:

  • Explain the challenges of interpretability of machine learning algorithms and why it's important.
  • Compare different interpretation techniques for machine learning and AI.
  • Balance the advantages and disadvantages of each technique.
  • Explain the challenges of interpretability of machine learning algorithms and why it's important.