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)))

Issue description

This issue was encountered when I was testing the python generated wrapper of Adaboost for my GSoC project for compatibility with scikit hyperparameter tuners in Python.

Steps to reproduce

I am pasting the code that I used but the changes that I am working on are still not merged into mlpack.
However, I am also pasting the best parameters and results that I go

What's your use case?
In other words, what's your pain point?

Variable names and their icons are shown as vertical header. This

• is ugly,
• doesn't show the selection properly,
• doesn't allow sorting by variable names,
• doesn't allow selection by dragging across a range of variables (though one can drag across rows in the table itself),
• and possibly something else.

<img wi

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

Dear team,

I am in stuck when convert very large numpy array to your TSDatasets.
These are what I have tried to fix my issue:

• when building time-series, I used tensorflow.keras.preprocessing.timeseries_dataset_from_array. After this step, the memory is still fine
• I concatenate all batch data into numpy array, this step produces problem so I use numpy memmap to avoi