adding OrdinalRidge and LAD regressors

[ghost]

This PR adds two new learners OrdinalRidge and LAD from mord library .

There are two things I would like to mention here:

• These learners do not have rescaled version because the predictions by these learners are already transformed within the range of zero to maximum of the label. Rescaling these transformed predictions makes the two predictions not correlate to each other. Here's the graph I plot between the predictions made by the OrdinalRidge and RescaledOrdinalRidge.
• In the unit tests, all the linear/non-linear regressors have 95% correlation with the labels. However, due to the transformed predictions by these learners, the correlation is only 0.85. I was trying to see if make_regression function would generate the data with labels in the given range (here I want the labels to be not less than 0 because predictions will have minimum 0 value), but I could not find such functionality.

I would like to get feedback on these and will work on making this better.

[codecov]

Codecov Report

Merging #687 (54def9d) into main (391cf34) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main     #687   +/-   ##
=======================================
  Coverage   96.85%   96.85%           
=======================================
  Files          63       63           
  Lines        9098     9102    +4     
=======================================
+ Hits         8812     8816    +4     
  Misses        286      286           

Impacted Files	Coverage Δ
skll/learner/__init__.py	97.09% <100.00%> (+<0.01%)	⬆️
tests/test_regression.py	99.64% <100.00%> (+<0.01%)	⬆️

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