My problem is that I want to show my random forest score through the predict.proba(x_test)[:, 1] method. However, it gives me this error code when I try use LIME to show the score using that prediction:
Cell In [31], line 3
1 i = 49
----> 3 exp = explainer.explain_instance(x_test.iloc[i], predict_fn_rf, num_features=5)
4 exp.show_in_notebook(show_table=True)
File ~\OneDrive\Documents\Anaconda\lib\site-packages\lime\lime_tabular.py:361, in LimeTabularExplainer.explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor)
359 if self.mode == "classification":
360 if len(yss.shape) == 1:
--> 361 raise NotImplementedError("LIME does not currently support "
362 "classifier models without probability "
363 "scores. If this conflicts with your "
364 "use case, please let us know: "
365 "https://github.com/datascienceinc/lime/issues/16")
366 elif len(yss.shape) == 2:
367 if self.class_names is None:
NotImplementedError: LIME does not currently support classifier models without probability scores. If this conflicts with your use case, please let us know: https://github.com/datascienceinc/lime/issues/16
Hope everyone is holding on tight, we're soon at Christmas!
Hi, as I have already made a post about the issue in detail over at stackexchange, I'll just post the link here and a quick summary of my problem:
Link here: https://datascience.stackexchange.com/questions/116799/problems-with-implementing-lime?noredirect=1#comment117787_116799
My problem is that I want to show my random forest score through the predict.proba(x_test)[:, 1] method. However, it gives me this error code when I try use LIME to show the score using that prediction:
Cell In [31], line 3 1 i = 49 ----> 3 exp = explainer.explain_instance(x_test.iloc[i], predict_fn_rf, num_features=5) 4 exp.show_in_notebook(show_table=True)
File ~\OneDrive\Documents\Anaconda\lib\site-packages\lime\lime_tabular.py:361, in LimeTabularExplainer.explain_instance(self, data_row, predict_fn, labels, top_labels, num_features, num_samples, distance_metric, model_regressor) 359 if self.mode == "classification": 360 if len(yss.shape) == 1: --> 361 raise NotImplementedError("LIME does not currently support " 362 "classifier models without probability " 363 "scores. If this conflicts with your " 364 "use case, please let us know: " 365 "https://github.com/datascienceinc/lime/issues/16") 366 elif len(yss.shape) == 2: 367 if self.class_names is None:
NotImplementedError: LIME does not currently support classifier models without probability scores. If this conflicts with your use case, please let us know: https://github.com/datascienceinc/lime/issues/16