Some classification problems do not have a balanced number of examples for each class label. For example, for a simple classification task, there might be 20 samples labelled frog, and 80 samples labelled airplane.

Stratification preserves this proportion of the each labels in each of the sets.

Stratified split is the correct choice when the data is skewed in terms of the labels, like the aforementioned example.

Last updated on Jun 01, 2022

Get AI confident. Start using Hasty today.

Automate 90% of the work, reduce your time to deployment by 40%, and replace your whole ML software stack with our platform.