Closed RaphaelS1 closed 1 year ago
@RaphaelS1 I've submitted new version here https://github.com/mlr-org/mlr3book/pull/508
There is still a lot of improvement to be made, but as the change of the FIFA -> penguins example has forced a lot of changes so I prefer to make this version v0.5 available
Next steps: exercises and further work on the structure
ranger_exp
object and briefly explaining the output at the end of this section.Regarding "10.4 suggest changing the analysis for prediction of good credit rather than bad to be consistent with the rest of the chapter. This confused me a few times when looking at the graphs because they seemed to show the opposite of what I expected."
The iml
section is ,,class neutral'' i.e. both classes are presented.
The counterfactuals
section shows what to do to move from bad
to good
, what is consistent with typical application of ,,what to do to get a credit''
The DALEX
section focuses on the 'probability of default' i.e. risk of not repaying credit - classical approach in credit scoring.
@larskotthoff can you confirm if comment from @pbiecek above needs to be addressed by you here or in the PR before we can merge?
Style
str
) https://github.com/mlr-org/mlr3book/blob/ac3f3b95eee0f4d1556734d83d05860596ad4c16/book/interpretation.qmd#L218explain_mlr3(data, y, label)
and explaining parameters, then move onto the example. https://github.com/mlr-org/mlr3book/blob/ac3f3b95eee0f4d1556734d83d05860596ad4c16/book/interpretation.qmd#L304-L307Content
Structure
Predictor$new