Comments (2)
Changed the label of this issue from bug
to enhancement
, where it may be useful in the future to create more clear explanations in the case of unexplained contributions > 1.
from argueview.
The unexplained contribution is simply the difference between the sum of the feature contributions and the prediction. While the prediction is contained within the percentage limits [-1, 1], the feature contributions are not. Hence, the difference is not either. Therefore it can occur that the unexplained contribution is over 100%.
That said, an absolute contribution that is larger than 1 indicates the underlying predictor is performing very badly and it is not clear if the current explanations are helpful to users to understand that the model is performing poorly.
from argueview.
Related Issues (10)
- In FeatureListVisualizer only show features that are above a given threshold HOT 2
- in FeatureListVisualizer also visualize the feature weights as bars
- Toulmin explanations HOT 3
- feature_importance_from_lime flips the weights outputted by lime
- List index out of range
- The leading rationale for the Toulmin visualer does not match the explanations output. HOT 2
- Interactivity should be optional
- Badge/bar color does not match feature direction HOT 5
- Possibility to have intercept value as text instead of bar HOT 1
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from argueview.