Model Validation — Resampling, AUC-PR & Calibration — Hands-on

Learn how to validate predictive models so that they generalize beyond the training dataset. This module covers data splits, resampling strategies, discrimination metrics (ROC, PR AUC) , calibration, uncertainty estimation and internal vs external validation, implemented in R and Python for omics and clinical ML workflows.

Model Validation — Resampling, AUC-PR & Calibration

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Session Index

Session 1 — Data Splits & Resampling Principles Session 2 — Discrimination Metrics, ROC & PR AUC Session 3 — Calibration, Uncertainty & Brier Score Session 4 — Internal, External Validation & Drift

Session 1

Fee: Rs 8800

Data Splits & Resampling Principles

Train, validation and test mindset

holdout vs k fold cross validation stratified splits and grouping by subject temporal and site based splits

Resampling strategies for performance estimation

k fold and repeated k fold CV bootstrap based validation nested CV for tuning vs evaluation

Data leakage patterns and how to avoid them

preprocessing inside pipelines batch effects and information bleed patient and family level dependence

Session 2

Fee: Rs 11800

Discrimination Metrics, ROC & PR AUC

Classification performance metrics

accuracy, sensitivity, specificity precision, recall, F1 confusion matrix interpretation

ROC and PR curves in imbalanced settings

ROC AUC vs PR AUC when PR AUC is more informative visualizing operating points

Regression metrics and error distributions

MSE, RMSE, MAE, MAPE R squared and adjusted R squared residual plots for model fit checks

Session 3

Fee: Rs 14800

Calibration, Uncertainty & Brier Score

Probability calibration concepts

well calibrated vs over confident models reliability diagrams and calibration plots calibration intercept and slope

Brier score and related measures

definition and interpretation relationship to calibration and sharpness integrated Brier score idea

Uncertainty estimation and confidence intervals

bootstrap confidence intervals for metrics prediction intervals for regression communicating uncertainty in reports

Session 4

Fee: Rs 18800

Internal, External Validation & Drift

Internal vs external validation strategies

bootstrap and cross validation as internal checks temporal and geographic external validation transportability considerations

Monitoring drift and performance over time

data and label shift concepts simple drift alarms and dashboards recalibration and model update policies

Deliverables: validation plan and summary report

tables of metrics with confidence intervals ROC, PR and calibration plots written validation narrative for publications

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Model Validation — Resampling, AUC-PR & Calibration Training | Biostatistics & ML for Omics

Model Validation — Resampling, AUC-PR & Calibration — Hands-on