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.
The current value of alpha value is hardcoded in many places to 0.05.

Describe the solution you'd like
Take this as a setup argument and use it everywhere for consistency. The default value can be 0.05.

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

That is a good suggestion. Another option is to have a keyword argument on fit which is a dictionary of estimator to kwargs to eliminate any potential for unnamed kwargs.

Originally posted by @camer314 in microsoft/FLAML#451 (comment)

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