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Bayesian regularization support in DALEX #445
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@asheetal size of the data shall not matter in the implemented XAI techniques (nor local nor global), |
In a recent experiment with p >> n what I did was as follows
It indeed helped. The final rank was a histogram against each predictor. I found that if I had run it once (l=1) I would have gotten completely inaccurate results. |
Forgot to add. The problem is not within DALEX. The problem is the model itself. For p >> n, the model must be Bayesian probabilitic. So must work in conjunction tfprobability etc models, so that now the variable importance is not a rank rather a probabilistic range of ranks. The researcher can now choose to decide how to infer the rank - median, max, min, overlapping. |
In some social science fields large data do not exist and researchers must make decisions using small number of samples (p >> n problem)
Good to see support in R (tfprobability, brnn packages)
Wondering if the DALEX team has any thoughts/comments on this?
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