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I am using dalex to plot multiple partial dependence plots in Python. It is a very useful package!
For some numeric features (especially those with outliers), I would like to specify the evaluation grid using option variable_splits in model_profile. However, if I do this, I need to specify a grid for all other features as well. Ideally, the function would use the keys in variable_splits only to overwrite the default. For those features without specified variable_splits key, it would resort to the default.
In the following case: the left-most feature should stop at a maximum of 10. The range for the other feature is fine though:
The text was updated successfully, but these errors were encountered:
Oops, wrong button ;-). Sounds reasonable! My suggestion only makes a difference if a variable list is passed as well. If there is no such list, it makes sense to only plot variables appearing in the keys.
Actually, I am not sure if it makes sense for the keys of variable_splits to override variables=None (like it is now). This behaviour was probably copied from R. I will investigate it.
I am using dalex to plot multiple partial dependence plots in Python. It is a very useful package!
For some numeric features (especially those with outliers), I would like to specify the evaluation grid using option
variable_splits
inmodel_profile
. However, if I do this, I need to specify a grid for all other features as well. Ideally, the function would use the keys invariable_splits
only to overwrite the default. For those features without specifiedvariable_splits
key, it would resort to the default.In the following case: the left-most feature should stop at a maximum of 10. The range for the other feature is fine though:
The text was updated successfully, but these errors were encountered: