FEA Implement pruning using honest subsample data to fit the leaves

[adam2392]

Changes proposed in this pull request:

• Adds honest pruning, so no empty leaves occur within the HonestTreeClassifier

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[codecov]

Codecov Report

Attention: Patch coverage is 77.50000% with 9 lines in your changes missing coverage. Please review.

Project coverage is 80.54%. Comparing base (e1c38ad) to head (d1221e4).
Report is 1 commits behind head on main.

Files with missing lines	Patch %	Lines
treeple/tree/_honest_tree.py	78.37%	3 Missing and 5 partials ⚠️
treeple/stats/forest.py	0.00%	0 Missing and 1 partial ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #286      +/-   ##
==========================================
+ Coverage   80.50%   80.54%   +0.03%     
==========================================
  Files          24       24              
  Lines        2334     2364      +30     
  Branches      339      343       +4     
==========================================
+ Hits         1879     1904      +25     
- Misses        318      319       +1     
- Partials      137      141       +4     

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[PSSF23]

Many of the errors revolve around:

ERROR treeple/tests/test_honest_forest.py - AttributeError: 'ClassifierTags' object has no attribute 'multi_output',

while the others are failing test_sklearn_compatible_estimator on "fitting with sample_weight is not equivalent to fitting with removed or repeated data points."

The first is due to the recent changes in sklearn 1.6 I assume? And I'll add some tests to skip in the others.

[PSSF23]

@adam2392 I skipped all the failing checks, and the package doesn't see any problems so far. Is there anything else to work on except adding tests for criterion? Do you have any example in mind?

[PSSF23]

The lack of test coverage is due to the expansion of honesty into regressors, which we currently don't have applicable cases.

[adam2392]

Merging. Thanks for the in-depth review and fixes @PSSF23 !