Comments (1)
See #67. This is not a bug - it is expected, as we rely on random sampling. If you want the explanation to be the same every time, you need to set a random seed.
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Related Issues (20)
- Is this Repo Maintained? HOT 2
- Can LIME only be used for text classification tasks?
- How to explain prediction for a data with just a few features (from all features of training dataset)? HOT 2
- Trying to explaine a simple Neural Network using LIME HOT 1
- submodular pick
- ans = self.domain_mapper.map_exp_ids(self.local_exp[label_to_use], **kwargs) KeyError: 1
- Getting error when my predict_fn is actually a method from a class HOT 1
- `show_in_notebook` shows no bars
- importance score for all tokens
- Unexpected keyword argument 'progress_bar' for lime_image.LimeImageExplainer().explain_instance()
- Issues with LIME Implementation in Image Classification
- How to use lime with 3 dim time series data and lstm
- Distances derivation, explain_instance() method in Tabular LIME
- How to handle binary features
- RecurrentTabularExplainer for regression task
- Question: using LIME for text generation HOT 1
- LIME and multi-correlated variables
- update Python support
- Inverse scaling/ normalization to get actual unscaled values in explanation : Old issue but I made a way around
- Met the issue about TypeError when using lime_image.LimeImageExplainer()
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