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.
With the March release of time series module, we have the ability to plot multiple forecasts in a single plot. This works ok if we have different models (such as from the output of compare_models). But sometimes, we want to compare different versions of the same model in which case, we get the same legend

from py

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

I published a new v0.1.12 release of HCrystalBall, that updated some package dependencies and fixed some bugs in cross validation.

Should the original pin for 0.1.10 be updated? Unfortunately won't have time soon to submit a PR for this.

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