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)))

As discussed yesterday in the IRC, some of the bindings are by default being built with cmake and it is cumbersome to manually switch them off since for most cases, people don't need them. So it might be a good idea to disable all of them by default.
For this, there are to approaches :-

1. To disable all the bindings by default. (suggested by @rcurtin and definitely a valid approach)
2. To add

• 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 "

String representations of dataset objects are used for previewing their contents from the terminal. When converting a Dataset object to a string, we build a table using ascii characters. The current table has fixed width columns that do not take full advantage of the terminal real estate if the dataset only contains a few columns.

echo $dataset;

<img width="574" alt="Annotation

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