Sebastian Raschka

hah, good catch!

Thanks for sharing your ideas on this! So, just to make sure that I understand correctly: You have a dataset where samples have NaN's in certain features. However, you don't...

No worries! I must say that your problem sounds like a very interesting one. I don't think it's unique; I'd even say that it's a very common one! However, the...

As far as I am aware, there's no recommended solution to that problem, yet. I just reopened that issue in case someone wants to take a knack at it, because...

> Q1. Is it possible to change the figure size of the plot? Yes, that's definitely possible. I think this is among the oldest functions in mlxtend, and the API...

Wow, this is awesome -- it even includes the support for both the level-2 classifiers as well as the meta-classifier. Again, thanks so much for the PR. I am happy...

I think that includes all of the ensemble methods I could currently think of as well :). I was/am a bit busy due to paper deadline end of the week,...

Just a very minor suggestion, could you change the docstring for the fit_params in `fit()` to the following: fit_params : dict of string -> object, optional Parameters to pass to...

Yeah, I think that also "fit_params : dict of string -> object, optional" would only be minimally more helpful compared to "dict" (found it in the GridSearchCV docs; http://scikit-learn.org/stable/modules/generated/sklearn.model_selection.GridSearchCV.html).

> Example 3 - Stacked Regression with sample_weight Yeah, I think this would be very useful! Maybe, "Example 3 - Stacked Regression with sample_weight using `fit_params`"? The reason is that...