scikit-learn

Currently code like this is repeated several times:

field = mapping.STORAGE_TENSOR_TYPE_TO_FIELD[
    mapping.TENSOR_TYPE_TO_STORAGE_TENSOR_TYPE[data_type]]
getattr(tensor, field)

This is repetitive and can be encapsulated in a helper function.
Also the name "storage tensor type" is misleading and has led to at least one bug.

We should:

1. Add a function that does all this

From this StackOverflow question it appears that it would be useful to modify the docstrings for dask.dataframe.Series.str.split.

The docstrings currently mention "

Scikit-learn provides multi-class options for area under curve: https://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html

We should provide the most common ones, such as the OVO Macro averaging used by Auto-Gluon.

• This primitive determines the day of the year a datetime column falls into
• https://pandas.pydata.org/docs/reference/api/pandas.Timestamp.dayofyear.html

Would be a good idea to include the nnetar algorithm from forecast package in R.

https://www.rdocumentation.org/packages/forecast/versions/8.16/topics/nnetar

Found a similar library here https://github.com/ourownstory/AR-Net. We can build an adapter on top of this.

Yes

Related: awslabs/autogluon#1479

Add a scikit-learn compatible API wrapper of TabularPredictor:

• TabularClassifier
• TabularRegressor

Required functionality (may need more than listed):

• init API
• fit API
• predict API
• works in sklearn pipelines

readthedocs analytics says that we have several search results that yield little or no useful results. Let's improvethose:

• gpu (only 2 results): make sure that explanation of device parameter mentions gpu as well
• gridsearch (0 results): make sure to include the term gridsearch in the meta data of

Hello everyone,

First of all, I want to take a moment to thank all contributors and people who supported this project in any way ;) you are awesome!

If you like the project and have any interest in contributing/maintaining it, you can contact me here or send me a msg privately:

• Email: nidhalbacc@gmail.com

PS: You need to be familiar with python and machine learning