Ngoni-M/TabPFN_project

Reproducible pipelines for TB classification using TabPFN and traditional machine learning models. # TB Classification with TabPFN and Traditional ML Models ## Project Overview **Title:** Comparing traditional machine learning algorithms with a transformer-based model (TabPFN) for the prediction of health outcomes This project compares **TabPFN**, a pre-trained transformer for tabular data, to **traditional machine learning models** (LightGBM, glmnet) in predicting **tuberculosis (TB) status** from host analytes. **We evaluate performance using:** - ROC curves and AUC - Accuracy, Sensitivity, Specificity, Balanced Accuracy **The comparison is performed for:** - All 22 analytes - Top 3 analytes selected using information gain **Dataset details:** - Concentrations of TB biomarkers measured using the Luminex assay - Clinical dataset (patient-level data) - Binary classification: patient is TB positive or TB negative - ML algorithms: Elastic Net Logistic Regression (glmnet), LightGBM, TabPFN (transformer-based) - Purpose: Compare performance of traditional ML vs transformer-based models - Notes: Clinical dataset not shared publicly; code can run on synthetic or similar datasets ## AIM Compare TabPFN to traditional ML models in predicting TB status from host analytes, using ROC, AUC, and balanced accuracy as performance metrics. ## Workflow 1. Load TB dataset and analyte information 2. Split data into training and test sets 3. Preprocess data: - Scale features - Apply SMOTE to balance classes 4. Select features for analysis: - All 22 analytes - Top 3 analytes (based on information gain) 5. Train traditional ML models on training data: - LightGBM - glmnet - Rpart (optional) 6. Tune hyperparameters using nested cross-validation 7. Prepare TabPFN inputs using training/test sets 8. Train TabPFN classifier on same training data 9. Generate predictions and probabilities for all models 10. Evaluate performance: - ROC curves and AUC - Accuracy, Sensitivity, Specificity, Balanced Accuracy 11. Compare TabPFN to traditional ML models: - Plot ROC curves together - Summar …