Regression Model Explanation

Explore how to build and interpret explainable regression models using H2O tools. Understand key methods like variable importance, Shapley values, residual analysis, and interpret visual plots to evaluate model performance and feature impact.

We'll cover the following...

Feature importance
Shapley values
Residual analysis
Implementation

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
Model performance explanations (confusion matrix, model correlation heatmap) ...

1.Introduction to Machine Learning

2.Supervised Learning: Regression Models with H2O

3.Supervised Learning: Classification Models with H2O

4.Unsupervised Learning: Clustering with H2O

5.Unsupervised Learning: Anomaly Detection with H2O

6.Closing Notes

7.Appendix

Regression Model Explanation