I was building a workflow to train an H2O Gradient Boosting Learner and predict SHAP values with it.
I don’t know what I changed, but now some of the values generated by my workflow are way out of tolerance, meaning that some are below or above -1.0 / + 1.0 which of course doesn’t make sense.
Any suggestions on what I could do to fix it? What information do you need to help me?
The SHAP loop node itself receives 100 samples to explain, 100 samples to permutate from, and it’s explanation set size was set to 100, too.