Cross Validation in Machine Learning - Bias-Variance Trade-off and Validation Techniques

Explore the essential concept of cross validation in machine learning through this comprehensive 23-minute lecture. Delve into the bias-variance trade-off on test data sets, understand the importance of training and validation data sets, and learn about various cross validation techniques. Examine the Validation Set Approach with practical examples, and discover sampling methods for small data sets. Gain insights into Leave-one-out-cross-validation (LOOCV) and its applications, and master the k-Fold Cross Validation technique with illustrative examples. Enhance your understanding of model evaluation and selection processes to improve the performance and reliability of your machine learning models.

Intro
Motivation
Bias-Variance trade-off on test data set
Training and Validation data sets
Validation Set Approach: Example
Sampling for small data sets
Leave-one-out-cross-validation (LOOCV)
LOOCV: Example
k-Fold Cross Validation
k-fold CV: Example