Model Assessment: Metrics and Selection

This lecture covers the importance of choosing performance metrics for model assessment in binary classification, focusing on accuracy, precision, recall, and F-score. It also delves into model selection techniques, such as k-fold cross-validation and leave-one-out cross-validation, to estimate model performance. The instructor explains the concepts of bias and variance in model evaluation, emphasizing the trade-off between model complexity and performance. Strategies to handle skewed data distributions, like stratification and over/under-sampling, are discussed. The lecture concludes with insights on the impact of data volume on bias and variance, highlighting the significance of the bias-variance tradeoff in machine learning.