Brandon Greenwell

@simonschoe The only thing I can think of is the scale on which the Shapley values are being returned in each approach. For example, in a binary outcome in a...

Hi @trilliumtechnical, I wouldn't get too hung up on the terminology. This package computes Shapley values using a Monte Carlo approach (this is equivalent to the approach taken by several...

We are in the process of writing a paper that will hopefully bring more clarity to the landscape of R packages and Shapley value algorithms they implement: https://github.com/bgreenwell/rjournal-shapley.

@marboe123 Can you show what the output is from calling `predict(mymodel, newdata = data, predcontrib = TRUE)`? I suspect XGBoost returns a list or an array (one element for each...

Yes! I’ll fix the package to account for multi class models. But here you have a list with one component of Shapley values for each of your three class outcomes....

Hi @ilagith, thanks for the note, and glad you find the package useful. In theory you should be able to use one of the model-agnostic approaches (e.g., `method="permute"`). The challenge...

Will be fixed after removing the plyr dependency; see https://github.com/koalaverse/vip/issues/126.

Hi @martinctc, thanks for reaching out! I started a similar [issue](https://github.com/koalaverse/vip/issues/108) a while back regarding similar methodologies for linear regression that I plan on adding in the next release (see...

Hi @juliasilge, thanks for reporting the issue! I only get this error when setting `metric = "auc"` (e.g., seems to work fine with `metric = "accuracy"`, so seems to be...

Ahh, after taking a second look, I see you're passing an integer to `reference_class` which "requires" a character string specifying the positive class. Switching to `reference_class = "One"` does the...