Bethany Ebel

Hi @RobertSamoilescu. I can't show the code explicitly as it's confidential data. I'm predicting credit risk using a simple GBM model. The code is essentially exactly the same as the...

@RobertSamoilescu Thanks so much, this was very helpful. Eventually I reduced the dimensions to hidden_dim=8 and latent_dim=4. This reduced sparsity from around 9 or 10 to around 6 and 7....

Thank you for this, this was so helpful. A couple more questions: * When you use diverse=True to generate many counterfactuals eg.100 per instance, there is a different set of...

Thank you so much for this - I will implement it and let you know how it goes.

Hi @RobertSamoilescu, I hope you are well! I am implementing this method for a different model. The categorical columns are ordinal encoded but not one hot encoded. Do I need...

@RobertSamoilescu Thanks so much, this is really helpful. One more quick question - do missing values (NaN) need to be handled for use with Alibi packages, particularly with anchor_tabular?

@RobertSamoilescu Thanks so much for your help. I decided to implement both approaches and compare them. At the moment I one hot encoding my categorical variables before passing it through...

Hi @RobertSamoilescu With the encoded_xTrain I am one hot encoding all the data (so increasing the number of columns in the data too). I don't ordinally or label encode the...

Ahh okay thank you for this, this makes sense! I'm happy to only use Label Encoding as the variables are properly treated as categorical variables inn the process :) Do...

@RobertSamoilescu Thank you! I've implemented the autoencoder & it is working. The category map is working too! My code is erroring on fitting the explainer: ``` self.explainer = CounterfactualRLTabular(predictor=self.predict_fn, encoder=self.heae.encoder,...