Add CatBoost component and pipeline

[angela97lin]

Closes #200

Notes / to discuss:

• Better for performance to not encode categorical features (since it is capable of handling) but this means we limit our imputation strategies to those that work with both numeric and string values (currently most_frequent). Is this too limiting? (Would be nice to apply different impute strategies for different columns...)
• Similarly, while catboost doesn't require encoding categorical features, if we add a RF feature selector to our pipeline, we would need to encode the features... Not sure that SelectFromModel using catboost integrates smoothly because of sklearn's cloning of estimator. Perhaps we can add a feature selection method that doesn't require an estimator (separate PR?)
• What parameters to expose to the user? CatBoost has alias for many of them but I chose to use the names that best align with our current API to avoid confusion.
• CatBoost supports only numerical target labels, so some fiddling was required, see issue: CatBoostClassifier predict() returns type np.float64 irrespective of input type catboost/catboost#305
• CatBoost supports snapshotting, allowing models to continue training from a saved point. I've turned this off for now, for the sake of being able to remove the catboost_info metadata produced, but good to know. Could we use this when we're considering distributed training? Or at least follow a similar approach?

[codecov]

Codecov Report

Merging #247 into master will increase coverage by 0.15%.
The diff coverage is 100%.

@@            Coverage Diff             @@
##           master     #247      +/-   ##
==========================================
+ Coverage   97.14%   97.29%   +0.15%     
==========================================
  Files          96      102       +6     
  Lines        2974     3180     +206     
==========================================
+ Hits         2889     3094     +205     
- Misses         85       86       +1

Impacted Files	Coverage Δ
evalml/pipelines/components/estimators/__init__.py	100% <ø> (ø)	⬆️
evalml/pipelines/pipeline_base.py	98.6% <ø> (ø)	⬆️
evalml/pipelines/components/__init__.py	100% <ø> (ø)	⬆️
...eature_selection/rf_classifier_feature_selector.py	100% <100%> (ø)	⬆️
...lines/components/estimators/regressors/__init__.py	100% <100%> (ø)	⬆️
evalml/pipelines/__init__.py	100% <100%> (ø)	⬆️
evalml/utils/gen_utils.py	100% <100%> (ø)	⬆️
evalml/tests/pipeline_tests/test_rf_regression.py	100% <100%> (ø)	⬆️
evalml/pipelines/classification/__init__.py	100% <100%> (ø)	⬆️
...sts/pipeline_tests/test_catboost_classification.py	100% <100%> (ø)
... and 26 more

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[dsherry]

This is great! I left a bunch of little comments, but the only major change request I had was for the bootstrap_type parameter. I will go over the tests some more too, but after that I'd say let's ship it!

[dsherry]

Nice work! LGTM 👍

[dsherry]

Oops, I thought I approved this yesterday, but I still saw it in the list on slack. Re-approved!