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bio-machine-learning-omics-classifiers

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Builds classification models for omics data using RandomForest, XGBoost, and logistic regression with sklearn-compatible APIs. Includes proper preprocessing and evaluation metrics for biomarker classifiers.

When to use

• building diagnostic classifiers
• building prognostic classifiers
• classifying expression data
• classifying variant data
• multi-class classification
• feature importance extraction
• ROC curve visualization

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