Speed up catboost fit: change catboost default and automl parameter ranges

[dsherry]

Fix #979

Two changes to speed up catboost fit times:

• Update catboost estimator default value for n_estimators
• Lower min/max automl ranges for n_estimators and max_depth

Performance results and discussion here.

Hopefully we can do more tuning after this and open these ranges up some more.

We have other features on the roadmap which will help with this too.

[codecov]

Codecov Report

Merging #998 into main will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main     #998   +/-   ##
=======================================
  Coverage   99.86%   99.86%           
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  Files         179      179           
  Lines        9424     9424           
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  Hits         9411     9411           
  Misses         13       13           

Impacted Files	Coverage Δ
...ents/estimators/classifiers/catboost_classifier.py	100.00% <100.00%> (ø)
...onents/estimators/regressors/catboost_regressor.py	100.00% <100.00%> (ø)

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[dsherry]

Interesting to note we already had the regressor set with this range, but not the classifier.

[jeremyliweishih]

LGTM. Its going to be fun with more experimentation later on!

[dsherry]

I was just testing #337 with the titanic dataset. I noticed that on main, the first batch took 14sec to fit, ~13sec of which was catboost. On ds_979_catboost_ranges_uniform, the first batch took ~3sec total. In both runs, catboost was the best model from the first batch, and got a pretty similar F1 score on the holdout: 0.953 before vs 0.948. Since that dataset is small I'd call that an even match.

[ctduffy]

Really like the analysis w the graphs and all of the data sets-excited to see the perf testing being used to change parameters to speed up evalml!

[freddyaboulton]

@dsherry Very cool analysis!

[eccabay]

LGTM!