adding basic outlier detection guardrail

[angela97lin]

Closes #138

[codecov]

Codecov Report

Merging #151 into master will increase coverage by 0.03%.
The diff coverage is 98.24%.

@@            Coverage Diff             @@
##           master     #151      +/-   ##
==========================================
+ Coverage   96.65%   96.69%   +0.03%     
==========================================
  Files          90       91       +1     
  Lines        2271     2328      +57     
==========================================
+ Hits         2195     2251      +56     
- Misses         76       77       +1

Impacted Files	Coverage Δ
evalml/models/auto_regressor.py	90.9% <ø> (ø)	⬆️
evalml/models/auto_classifier.py	100% <ø> (ø)	⬆️
evalml/models/auto_base.py	93.36% <100%> (+0.16%)	⬆️
...alml/tests/guardrail_tests/test_detect_outliers.py	100% <100%> (ø)
evalml/tests/automl_tests/test_autoclassifier.py	100% <100%> (ø)	⬆️
evalml/guardrails/utils.py	96% <95.45%> (-0.43%)	⬇️

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[angela97lin]

Notes:
After doing a bit of research, I combined IsolationForest from sklearn with IQR as per this article. Reason for doing so was that IsolationForest looked promising but required the user to give an estimate of contamination based on their data. With IQR, I instead will look at the scores returned from IsolationForest's decision_function and return the indices corresponding to any extreme score. Is this overkill for just a guardrail?

Also looked into just using IQR but thought that it may not be so great as we increase the number of features.

Definitely open to other suggestions!

[jeremyliweishih]

Do you think we can use IQR as a very basic outlier detection tool but also keep isolation forests as another?

[kmax12]

LGTM

[angela97lin]

@kmax12 per our brief discussion, should I start moving the guardrails out of Auto(*) classes? Or leave them in there for now?

Edit: per discussion, will push in for now and make one PR to remove all guardrails from Auto(*)