When baseline score is close to 0, set percent improved to nan

[dsherry]

Fixes #1458

Problem
In #1458 @rpeck found a dataset where, for the baseline model (which predicts the mean) ExpVariance came out to 0 on 2 of the 3 CV folds, but came out to ~1e-16 on the other CV fold. This resulted in the percent improvement score for ExpVariance for all pipelines being super big, because that computation divides by the baseline score for the objective in question.

Solution
Define a threshold (1e-10 seemed good) below which the percent improvement computation assumes the baseline objective score is essentially 0, and will then set the percent improvement for all other pipelines to nan.

[freddyaboulton]

Thanks @dsherry ! Looks great and I verified that percent_better is nan on ExpVar in the dataset mentioned in #1458 !

[dsherry]

Ah thanks for double-checking that @freddyaboulton ! Sweet.

[codecov]

Codecov Report

Merging #1467 (d33d57a) into main (e10cb02) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@            Coverage Diff            @@
##             main    #1467     +/-   ##
=========================================
+ Coverage   100.0%   100.0%   +0.1%     
=========================================
  Files         223      223             
  Lines       15013    15019      +6     
=========================================
+ Hits        15006    15012      +6     
  Misses          7        7             

Impacted Files	Coverage Δ
evalml/objectives/objective_base.py	100.0% <100.0%> (ø)
...lml/tests/objective_tests/test_standard_metrics.py	100.0% <100.0%> (ø)

---

Continue to review full report at Codecov.

Legend - Click here to learn more
Δ = absolute <relative> (impact), ø = not affected, ? = missing data
Powered by Codecov. Last update e10cb02...d33d57a. Read the comment docs.