Using LRC with elasticnet penalty

[bchen1116]

fix #2289 #2281
Replacement of 2381

Rather than using SGDClassifier for ElasticNet, we now use Logistic Regression with ElasticNet penalty.
Perfs here

Doc assertion passes for lead scoring!
Optimized with lead scoring:

Optimized with AUC:

[codecov]

Codecov Report

Merging #2420 (cb2c94b) into main (96abe4a) will decrease coverage by 0.1%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##            main   #2420     +/-   ##
=======================================
- Coverage   99.7%   99.7%   -0.0%     
=======================================
  Files        283     283             
  Lines      25321   25316      -5     
=======================================
- Hits       25221   25216      -5     
  Misses       100     100             

Impacted Files	Coverage Δ
evalml/tests/automl_tests/test_automl.py	99.8% <ø> (-<0.1%)	⬇️
evalml/tests/component_tests/test_components.py	100.0% <ø> (ø)
...valml/tests/pipeline_tests/test_component_graph.py	100.0% <ø> (ø)
evalml/tests/pipeline_tests/test_pipeline_utils.py	100.0% <ø> (ø)
evalml/tests/pipeline_tests/test_pipelines.py	99.8% <ø> (ø)
...ts/estimators/classifiers/elasticnet_classifier.py	100.0% <100.0%> (ø)
evalml/tests/component_tests/test_en_classifier.py	100.0% <100.0%> (ø)
...s/prediction_explanations_tests/test_algorithms.py	98.3% <100.0%> (-<0.1%)	⬇️

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[jeremyliweishih]

LGTM @bchen1116 awesome work! The only thing we could add to the unit tests is coverage to ensure NaNs don't show up again but we might be covered from the notebook.

[freddyaboulton]

Perf tests look good to me!

[chukarsten]

Per the n_jobs = 1 or -1, I think we're gucci. Great job getting this done.