Added new initil assumptions with boostings

[dmitryglhf]

This is a 🔨 code refactoring.

Summary

• New Initial Assumptions: Updated initial assumptions by adding boosting-based solutions (CatBoost, XGBoost, LightGBM).

  Comparison table between old and new assumptions (validated on automlbenchmark small dataset 1h8c):

  	Metric (mean)	main (Random Forest first)	new (GBM joined with Linear model)
  0	auc	0.869263	0.879746
  1	acc	0.84667	0.852339
  2	balacc	0.805336	0.822745
  3	logloss	0.449189	0.377827
  4	training_duration, s	242.554	251.445

Context

Closes #1341

[pep8speaks]

Hello @dmitryglhf! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! 🍻

Comment last updated at 2025-01-23 15:41:19 UTC

[dmitryglhf]

/fix-pep8

[github-actions]

All PEP8 errors has been fixed, thanks ❤️

Comment last updated at Thu, 23 Jan 2025 18:42:11

[dmitryglhf]

Discovered three most useful assumptions.

	Metric (mean)	main (Random Forest first)	gbm_linear	xgb_lgbm_linear	rf_in_fork
0	auc	0.869263	0.879746	0.877597	0.878225
1	acc	0.84667	0.852339	0.848727	0.85005
2	balacc	0.805336	0.822745	0.818386	0.819006
3	logloss	0.449189	0.377827	0.392734	0.396282
4	training_duration	242.554	251.445	213.057	246.447

Pipelines and full comparison table

1. GBM_Linear
   

2. XGB_LGBM_Linear
   

3. RF_in_Fork
   

Full comparison table: full_comparison.xlsx

Full tables for each pipeline:
gbm_catboost_new_params.csv
main_rf.csv
rf_in_fork.csv

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 80.68%. Comparing base (6dd93b2) to head (326d485).

Additional details and impacted files

@@            Coverage Diff             @@
##           master    #1359      +/-   ##
==========================================
+ Coverage   80.13%   80.68%   +0.55%     
==========================================
  Files         146      146              
  Lines       10512    10512              
==========================================
+ Hits         8424     8482      +58     
+ Misses       2088     2030      -58     

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[dmitryglhf]

@nicl-nno Среди трех начальных приближений стоит оставить все или же только то, которое улучшает метрики больше остальных?

[nicl-nno]

Среди трех начальных приближений стоит оставить все или же только то, которое улучшает метрики больше остальных?

Зависит от того, меняется ли лидер при смене группы датасетов. Если везде +-один вариант доминирует - то можно его иоставить.