regression

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)))

Is your feature request related to a problem? Please describe.
A clear and concise description of what the problem is.

to_csv is slow and cost lot storage

Describe the solution you'd like
A clear and concise description of what you want to happen, ideally taking into consideration the existing toolbox design, classes and methods.

use to_feather to make it more efficient and sm

Hi,

There are still parts of boost library that are used inside mlpack, more specifically in here

• mlpack/src/mlpack/core/tree/cosine_tree/cosine_tree.hpp boost::heap
• mlpack/src/mlpack/core/data/load_arff.hpp boost::tokenizer
• mlpack/tests/main_tests/emst_test.cpp boost::math::iround()
• mlpack/core/tree/cosine_tree/cosine_tree.cpp boost::m

Python 3.10 has been released. We should test it. If all the dependencies support it, we should add it to CI.

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