Why is stratification metrics needed?

The stratification metric is used to assess the quality of data partitioning into strata (layers) to improve analysis or model training. It helps understand how well the data is distributed across groups, ensuring each stratum is homogeneous and representative.

For example, in stratified sampling in machine learning, the metric indicates how well the class proportions in the sample match the original data, which is important for preventing bias and improving model quality.

Thus, the stratification metric is needed to control and optimize the data partitioning process, which affects the performance and accuracy of algorithms.