Update scikit-learn to 0.24.1

[desilinguist]

This PR closes #653.

It pretty much works out of the box except for two changes:

• Setting the new keyword argument error_score to "raises" in the GridSearchCV() call made in Learner.train() since we want to raise an exception if there was any problem with fitting the estimator. This change is necessary because the new scikit-learn default is to simply return a nan as the fit score in case of a problem which does not work for us.

• LinearRegression models in scikit-learn now support a new keyword argument positive which can be set to True to use Non-negative Least Squares (NNLS) regression. This is probably something we want to enable in SKLL since it could be useful in RSMTool. This required the fix for Learner._check_input_formatting() does not work for dense featuresets #656 which has already been merged.

• Add a test for this new non-negative regression.

• Other minor changes:

  • Since Python 3.6 is so long in the tooth, I have changed the Linux builds (on Travis) to use Python 3.7 and the Windows builds (on Azure) to use Python 3.8. I am using Python 3.9 locally.

  • Update both requirements.txt and conda_requirements.txt to use the new version of scikit-learn.

[codecov]

Codecov Report

Merging #659 (1b4b870) into main (a9321a4) will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##             main     #659   +/-   ##
=======================================
  Coverage   95.09%   95.09%           
=======================================
  Files          27       27           
  Lines        3101     3101           
=======================================
  Hits         2949     2949           
  Misses        152      152           

Impacted Files	Coverage Δ
skll/learner/__init__.py	96.47% <ø> (ø)

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update a9321a4...1b4b870. Read the comment docs.

[mulhod]

Awesome!