Regression Model Explanation
Gain insights into machine learning models, including the importance of features, residual analysis, and Shapley values.
We'll cover the following...
Knowing how to build explainable machine learning models is very valuable. It helps us understand how the model makes decisions. It promotes transparency and accountability, builds trust in machine learning models, and helps us identify and mitigate potential biases or errors in the model.
In this lesson, we’ll explore the airline fare predictor model using the explainable modules available in the H2O package to gain more insights. It provides us with various methods that explain machine learning, like:
Variable importance
Partial dependence plots
Residual analysis
Shapley values
Feature interactions
Individual conditional expectation (ICE) plot
Individual row prediction explanations ...