As announced last week, this week’s topic is Statistical/Machine Learning (ML) explainability using Kernel Ridge Regression (KRR) surrogates. The core idea underlying this type of ML explainability methods is to apply a second learning model to the predictions of the first so-called black-box model.
How am I envisaging it? Not by utilizing KRR as a learning model, but as a flexible (continuous and derivable) function of model covariates and predictions. For more details, you can read this pdf document on ResearchGate:
Statistical/Machine learning model explainability using Kernel RidgeRegression surrogates
Comments, suggestions are welcome as usual.
