Sebastian Raschka comments

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                                            Sebastian Raschka

mlxtend.evaluate for corrected resampled t-test from Nadeau's paper

Hi, there, no, I only implemented the standard procedure and haven't looked at this version from Nadeau's paper. Let's leave the issue open though since I want to read the...

mlxtend.evaluate for corrected resampled t-test from Nadeau's paper

> I highly recommend to use the corrected not sure about the corrected one (haven't checked it out yet), but the I agree that the "classic" resampled t-test definitely shouldn't...

mlxtend.evaluate for corrected resampled t-test from Nadeau's paper

Yeah, good point!

mlxtend.evaluate for corrected resampled t-test from Nadeau's paper

On a side note, while the 5x2cv t-test has a lower false positive as the resampled t-test (not sure about a comparison with the corrected resampled t-test), Tom Dietterich (the...

mlxtend.evaluate for corrected resampled t-test from Nadeau's paper

Thanks a lot for sharing. I should add it some time

Ensemble Voting Regressor

Thanks for the note, if it's not already supported, that'd be a good addition! Personally, I am currently stretched very thin this semester, but I will happy to revisit some...

_AxesBase.axis() is called with incorrect arguments in plot_decision_regions()

Thanks for the note, it looks like there's been some change in matplotlib recently (didn't get the warning in previous version). I can patch that via a PR

stacking_cv_regression.py should use X, y = check_X_y(X, y, accept_sparse=['csc', 'csr'], dtype=None)

Thanks for the note! I think the current implementation should work fine though: ```python from sklearn.utils import check_X_y import numpy as np X = np.random.random((10, 2)).astype(str) y = np.ones(10) check_X_y(X,...

stacking_cv_regression.py should use X, y = check_X_y(X, y, accept_sparse=['csc', 'csr'], dtype=None)

Thanks for the note. Yes, the idea is that the models should take care of that. One might argue that this is also true for "np.inf". So, we could change...

stacking_cv_regression.py should use X, y = check_X_y(X, y, accept_sparse=['csc', 'csr'], dtype=None)

Yeah, I think it may not be necessary to recast the inputs to NumPy arrays ... depending on the behavior of the estimators used within the stacking classifier. Regarding the...