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The code has multiple reference to web addresses using "http://" instead of the more secure "https://". Is it not better to switch it to https uniformly?

This is a suggestion to simplify reporting the dask version in the issue template by providing a suggested code snippet to run (in the Environment section of the issue template):

import dask; print("- Dask version:", dask.__version__)
import sys; print("- Python version:", sys.version)

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.

• As a user, I wish featuretools dfs would take a string as cutoff_time aswell as a datetime object

Code Example

fm, features = ft.dfs(entityset=es,
                      target_dataframe_name='customers',
                      cutoff_time="2014-1-1 05:00",
                      instance_ids=[1],
                      cutoff_time_in_index=True)

as well as

Currently, there is a lot of repetitive boilerplate in the datasets modules, and no tests for individual loaders such as load_PBS_dataset, load_japanese_vowels, etc

What should be done here:

• reduce boilerplate, refactor common code into single location
• ensure function signatures are the same, deprecate if needed
• add an argument return_mtype such as in the synthetic data gener

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