Episode Synopsis "Stratified Sampling"

Stratified sampling provides a mechanism by which to split a larger dataset into smaller pieces. While random approaches are commonly used, stratified sampling ensures a relatively consistent distribution. This can result in an unwanted loss of variance, but can also beneficially reduce variance.

Listen "Stratified Sampling"

More episodes of the podcast Machine Learning Bytes

• K-Fold Cross Validation
• Stratified Sampling
• Boosting
• Bagging
• Bias, Variance, and the Bias-Variance Tradeoff
• Empirical Risk Minimization