Add pipeline threshold to confusion matrix returns

[bchen1116]

fix #3079

There are 2 potential issues that I want to raise, although they are not severe enough to block this PR from being merged.
First is the aesthetic of the index when it is very granular, as can be seen here:

Note that the threshold is 0.99999..., but looking at the dataframe, it just shows as 1.0, which could be confusing for OS users. By grabbing the index itself,, we can see the actual value. We could stringify the index to have them appear, but this might be annoying for our internal use:

The other is when our optimized pipeline threshold doesn't match the best threshold chosen through this method. This can be seen in the problem above, but also can be seen here:

Above, the pipeline is optimized using accuracy binary, and we see the pipeline threshold 0.360321 actually has a worse performance value compared to 0.5 with accuracy, which is the ideal value find_confusion_matrix_per_thresholds finds. The differences here are likely how we're finding the optimal threshold, with our optimize_thresholds using gradient descent, and our current method using a simple linear scan. Is this disparity an issue for our users? Discussing with @freddyaboulton, it could be confusing when our optimal thresholds don't match up. However, what would be the best approach to fix this, if necessary?

Again, these two issues shouldn't block the merge of this PR, but are both things I wanted to bring up for discussion.

[codecov]

Codecov Report

Merging #3080 (76b418e) into main (2772536) will increase coverage by 0.1%.
The diff coverage is 100.0%.

@@           Coverage Diff           @@
##            main   #3080     +/-   ##
=======================================
+ Coverage   99.8%   99.8%   +0.1%     
=======================================
  Files        313     313             
  Lines      30470   30483     +13     
=======================================
+ Hits       30380   30393     +13     
  Misses        90      90             

Impacted Files	Coverage Δ
evalml/model_understanding/decision_boundary.py	100.0% <100.0%> (ø)
...odel_understanding_tests/test_decision_boundary.py	100.0% <100.0%> (ø)

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[chukarsten]

This looks good to me!

[angela97lin]

Interesting, thanks for outlining these two concerns! Is the first issue because of the way pandas internally handles things? aka it just rounds up rather than printing the actual value? If so, I'd say it's not a big deal given the actual index is still what we expect it to be.

[bchen1116]

@angela97lin yep, that was the first concern! Just an aesthetic issue. Filed an issue to address the second here

[ParthivNaresh]

Looks good man, thanks for filing an issue for the second point. I think we're fine with the aesthetic issue for now.