3324 - Interpreting Variable Relationships - 2019

Issued by GAIn® - The Global AI network

Our two-day training begins with a summary of different methods to measure and interpret the importance of different variables within traditional machine learning and statistical models. Using SHAP-values you will make intuitive visualizations of variable importance and interdependencies. Rocketing your success and ability to understand and explain both your model output and variable interactions.

Skills

Earning Criteria

Variable importance – Understand and measure variable importance for machine learning models
Using SHAP-values – Can interpret complex black-box models using SHAP
Introduction to graph theory – Understand and explain how graph theory can be used to visualize classification problems
Fundamentals of Bayes’ theorem – Know the conditional probabilities and chain rules of Bayes’ theorem
Understanding Bayesian Networks – Capable of interpreting the results of a Bayesian network
Basic classification networks – Perform classification and interpret results using Naïve-Bayes
Advanced classification networks – Use TAN and TBNL for classification problems
Exam