Hi I would like to propose a better implementation for 'test_indices':

We can remove the unneeded np.array casting:

Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))

Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))

What is the desired addition or change?

A parameter check should be added to all bindings that checks for the correct relative input shapes. For example the training data and training labels must have the same number of samples, testing data and training data must have the same number of features etc.

What is the motivation for this feature?

Refer to #2818 for more information and

• What's wrong?

Rank widget does not show that Manual selection is active, if:

1. Best ranked was active.
2. I clicked into the white part of the table to deselect.

The "Select Attributes" radio button still has "Best ranked" active, but nothing is selected (and the status bar shows that nothing gets to the output). Instead, the "Select Attributes" radio button should switch to "

The PR JuliaData/CategoricalArrays.jl#310 means that an array with elements of type Symbol can no longer be wrapped as a CategoricalArray.

This means all MLJ documentation and test code that uses symbols in categorical data must be refactored to use strings instead.

These repos, at least, need checking/refactoring, in order of priority:

• MLJ

• [x]

Hi @JavierAntoran @stratisMarkou,

First of all, thanks for making all of this code available - it's been great to look through!

Im currently spending some time trying to work through the Weight Uncertainty in Neural Networks in order to implement Bayes-by-Backprop. I was struggling to understand the difference between your implementation of `Bayes-by-Bac