feat: add metrics module with classification metrics

[IndexSeek]

I wanted to start making some progress on supporting a new ibis_ml.metrics
module might look like with the first pass to support classification metrics.

In this initial pass, the functions accept the y_pred and y_true as Ibis table
expression columns.

A few thoughts

• Should we go ahead and try this with regression metrics? Or would it be okay
  to start with classification only as an "experimental" type of op.
• Do we need a "metrics" folder that contains "regression" and "classification" files?
• Is there a more effective way to test this than to compare it to scikit-learns metrics?

What could go wrong?

I anticipate the inputs are coming from the same table expression, although testing
this behavior (or at least documenting) with what happens with input from two different
table expressions would be necessary.

Resolves (partially) #174

Edit: As far as testing goes, I wonder if more precision might be necessary.

[codecov-commenter]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 90.57%. Comparing base (7d2d021) to head (a236949).

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #176      +/-   ##
==========================================
+ Coverage   90.35%   90.57%   +0.22%     
==========================================
  Files          29       31       +2     
  Lines        2083     2132      +49     
==========================================
+ Hits         1882     1931      +49     
  Misses        201      201              

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[deepyaman]

Sorry, I didn't see this earlier; will try to take a look soon!