Add comprehensive model support with XGBoost, LightGBM, and advanced …

[dgenio]

…features

• Add ModelFactory for creating classifiers and regressors
• Add ModelEvaluator for cross-validation and performance assessment
• Add ModelSelector for hyperparameter tuning (grid/random search)
• Add support for XGBoost, LightGBM, and HistGradientBoosting
• Add get_model_recommendations for intelligent model selection
• Add create_model_ensemble for ensemble methods
• Include comprehensive error handling and validation
• Add automatic model recommendations based on problem characteristics
• Update init.py to export model functions
• Add comprehensive test suite for all model functionality

Pull Request

📋 Description

Type of Change

• 🐛 Bug fix (non-breaking change which fixes an issue)
• ✨ New feature (non-breaking change which adds functionality)
• 💥 Breaking change (fix or feature that would cause existing functionality to not work as expected)
• 📚 Documentation update
• 🧹 Code refactoring (no functional changes)
• ⚡ Performance improvement
• 🧪 Test improvements
• 🔧 Build/CI improvements

🔗 Related Issues

• Fixes #
• Related to #

🧪 Testing

Test Coverage

• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• I have added integration tests if applicable

Manual Testing

• Tested locally with make check
• Tested with example scripts
• Tested edge cases

Test commands run:

# List the commands you used to test
make check
python examples/quickstart.py

📝 Changes Made

Code Changes

API Changes

• No API changes
• Backward compatible API additions
• Breaking API changes (requires major version bump)

API changes:

📚 Documentation

• I have updated the documentation accordingly
• I have updated docstrings for new/modified functions
• I have added examples if applicable
• I have updated the CHANGELOG.md

✅ Checklist

Code Quality

• My code follows the project's style guidelines
• I have performed a self-review of my own code
• I have commented my code, particularly in hard-to-understand areas
• My changes generate no new warnings
• I have run ruff check and ruff format
• I have run mypy type checking

Testing & CI

• I have added tests that prove my fix is effective or that my feature works
• New and existing unit tests pass locally with my changes
• All CI checks pass
• Code coverage is maintained or improved

Documentation & Communication

• I have made corresponding changes to the documentation
• My commit messages follow the conventional commit format
• I have updated the CHANGELOG.md if applicable

🔍 Review Notes

Focus Areas

Questions for Reviewers

📸 Screenshots/Examples

# Example usage of new feature
import skdr_eval

# Your example here

🚀 Deployment Notes

• No special deployment considerations
• Requires database migrations
• Requires environment variable changes
• Requires dependency updates

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Additional Context:

[Copilot]

Pull Request Overview

This PR adds comprehensive model support to the skdr-eval library by introducing new classes and functions for machine learning model creation, evaluation, and selection. The changes include support for additional algorithms like XGBoost, LightGBM, and HistGradientBoosting along with intelligent model recommendations and ensemble methods.

Key changes:

• Added ModelFactory, ModelEvaluator, and ModelSelector classes for comprehensive model management
• Implemented support for advanced models (XGBoost, LightGBM, HistGradientBoosting) with graceful fallbacks
• Added intelligent model recommendation system and ensemble creation capabilities

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 1 comment.

File	Description
src/skdr_eval/models.py	New module containing core model functionality including factory patterns, evaluation, and selection
tests/test_models.py	Comprehensive test suite covering all model functionality with error handling and integration tests
src/skdr_eval/init.py	Updated exports to include new model functions and classes

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[Copilot]

The all list contains many imports that are not present in the import statements at the top of the file. Only the functions and classes that are actually imported should be included in all to avoid import errors.