RobertSamoilescu

Decided to remove the options `response_method='auto'`. This is because for a binary classifier the number of output targets can change based on the other parameters (e.g., `method='recursive'` and `kind='average'` vs...

Also decided to remove `method='auto'` option. The `sklearn` logic is the following: ```python if method == Method.AUTO: if isinstance(self.predictor, BaseGradientBoosting) and self.predictor.init is None: method = Method.RECURSION.value elif isinstance(self.predictor, (BaseHistGradientBoosting,...

@jklaise, check the note at the end of this [section](https://scikit-learn.org/stable/modules/partial_dependence.html#computation-methods) which confirms that the two methods differ in the values they return

Hi @HeyItsBethany3. It would be ideal if you can share some code/notebook and the data to have more context and identify the problem. Thank you!

One reason for poor sparsity would be a bad autoencoder. If the reconstruction is bad, I would expect the counterfactuals to be far from the input. Some things to look...

1. When using a heterogeneous dataset (i.e., a dataset having both numerical and categorical variables) then you need to use the heterogenous autoencoder as in the [adult example](https://github.com/SeldonIO/alibi/blob/master/doc/source/examples/cfrl_adult.ipynb). The output...

1. To make the diversity run deterministic, you can simply set the seed through `numpy` as follows: ```python np.random.seed(0) explanation = explainer.explain(X=X, Y_t=Y_t, C=C, diversity=True, num_samples=100, batch_size=10) ``` As mentioned...

@HeyItsBethany3, I believe that you have two options: 1. treat ordinal features as categorical and use one-hot-encoding -- same as you did before. 2. since the features are ordinal, there...

@HeyItsBethany3, please check issues #391 and #516. As you see in issue #391, AnchorTabular does not work with `NaN` values. You can either drop the rows or use your preferred...

@HeyItsBethany3, I don't understand exactly why you are trying to do with the `encoded_xTrain = self.encoder.encode(original_data)` ... I would say that this is not correct and it is probably where...