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I'm trying to use the package with an H2O xgboost model (I've also tried it with GBM and get the same thing. The error is:
Error in glm.fit(x = x_fit, y = y, weights = weights, family = gaussian()) :
NA/NaN/Inf in 'x'
Here is the code I'm running:
explainer <- lime::lime(as.data.frame(wellnessTrain), mdl)
explanation <- lime::explain(as.data.frame(wellnessTest),
explainer, n_labels = 1, n_features = 2)
This is caused by having some NA values in the data frame, but I thought that this had already been fixed in issue #8. I verified this by removing the three columns that have NA values as a test. These NA values are meaningful and H2O's GBM and XGBoost handle them by creating a category for the missing value after binning the unmissing feature values. Is there any easy fix here?
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