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Thanks to your shapviz package, we can do many beautiful visualizations of shap values in the R environment. Since there are many current machine learning algorithms, everyone tends to use a system wi…
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I'm trying to calculate SHAP-values of random forests from the ranger package. The random forests have between 1000 and 8000 trees and the datasets between 5000 and 37000 rows. The only model I manage…
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"mshap" importance bar plots could be simplified: Instead of using {patchwork}, we can go for stacked or dodged bar plots, just like in my answer here:
https://stackoverflow.com/questions/77279563/…
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I think it would be useful to have a function that computes/visualises the relative importance of interaction effects.
Here's an example for an xgboost model where SHAP interaction values are avail…
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## Summary
@StrikerRUS @guolinke @shiyu1994 @jmoralez @btrotta @Laurae2 could you please try to list out what you all feel is required before a v4.0.0 release of LightGBM is prepared?
Please edi…
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I try to explain long short-term memory (LSTM) model with keras package, but does not work. The codes are:
`library(keras)
library(kernelshap)
set.seed(123)
x
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kernelshap involves working with different matrices of different shapes.
We should consistently use feature names for the "p" dimension and prediction names (if available) for the "K" dimension. Th…
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I am trying to unify my random forest model using the available function but am consistently getting the same error, "Error in Prediction/n : non-numeric argument to binary operator"
The random for…
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Thanks for providing a great SHAP visualisation package for R!
I'm looking into fast ways to surface interaction effects in H2O GBMs. Unfortunately, unlike xgboost, H2O does not provide interaction…
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Hi
We have ran a analysis on a multiclass problem:
```R
X = bake(prep(recipe), new_data = train_data)
X_pred = bake(prep(recipe), has_role("predictor"), new_data = train_data, composition = "m…