Model Evaluation and Selection Done Right: CV, Tuning, Leakage

Your model scored 95 percent. Can you trust it? Probably not yet. In this episode of Machine Learning from Zero to Advanced, we build a trustworthy evaluation workflow step by step: cross-validation, stratified, group and time-series splits, grid, random and Bayesian search, learning and validation curves, bootstrap confidence intervals, target leakage, nested cross-validation, error analysis and reproducible experiments, with worked numbers and scikit-learn code. Next episode: time series and forecasting. Subscribe so you do not miss it. 0:00 Intro 0:50 Why One Split Fails 1:47 Cross-Validation 4:01 Special Splits 5:52 Hyperparameter Search 7:45 Learning and Validation Curves 8:41 Statistical Uncertainty 10:04 Leakage and Nested CV 11:36 Error Analysis and Reproducibility 12:52 Recap and Next Steps #MachineLearning #CrossValidation #DataScience #scikitlearn #ModelEvaluation