Metrics for Classification

This lecture covers the estimation of data points through sampling, the concept of cross-validation to confirm experimental findings, quantifying performance using metrics like True Positives and False Negatives, determining the optimal model, detecting overfitting, and the sensitivity of performance to various factors in classification.