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Update from task 2064bbbd-4fcf-4e5c-98d7-278b9d52f56f - #8

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pydevcasts wants to merge 5 commits into
prostate-cancer-bcr-prediction-enhancement-8b910from
investigation-of-suspicious-codes-2f56f
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Update from task 2064bbbd-4fcf-4e5c-98d7-278b9d52f56f#8
pydevcasts wants to merge 5 commits into
prostate-cancer-bcr-prediction-enhancement-8b910from
investigation-of-suspicious-codes-2f56f

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This PR was created by qwen-chat coder for task 2064bbbd-4fcf-4e5c-98d7-278b9d52f56f.

qwen-intl and others added 5 commits August 31, 2026 21:53
-Key features implemented:
-Updated default model names in compare_models_nested_cv from "RandomForest" and "LogisticRegression" to "Random Forest" and "Logistic Regression" for consistent naming convention
-Modified core/src/pipeline.py to ensure proper string matching with build_model function requirements
-Fixed potential model building failures due to inconsistent naming between comparison function and model factory

The changes ensure proper model instantiation across the pipeline by maintaining consistent naming conventions between the comparison utility and the underlying model building system.
…workflow

Key features implemented:
- Added comprehensive .gitignore files for core project and root directory to exclude data files, models, cache, and IDE artifacts
- Refactored feature selection from complex 3-layer pipeline to simpler MI-based approach with variance threshold and 7 engineered features
- Updated feature selection notebook (04) to use simplified pipeline and properly handle clinical/engineered feature separation
- Modified model training notebook (05) to load fitted selector artifacts and apply consistent feature transformation to test data
- Implemented proper data transformation workflow including imputation, variance filtering, and MI selection for test/external datasets
- Added engineered feature creation logic with Gleason scores, pathway analysis, and clinical interactions

The changes streamline the feature selection process while maintaining domain-specific engineering, improve test data handling consistency, and resolve previous pipeline errors related to feature name mismatches between training and test sets.
Key features implemented:
- Refactored feature_selection.py to use simple MI-based pipeline instead of 3-layer approach
- Updated notebooks 04 and 05 to align with simplified feature selection workflow
- Fixed ValueError in transform_selected by ensuring feature alignment between fit and transform
- Added core/.gitignore file to exclude data files and outputs from version control
- Modified .gitignore to properly track source files while ignoring compiled outputs

The changes resolve the feature name mismatch error during transformation by implementing consistent feature alignment and simplifying the overall feature selection architecture.
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