StratifiedGroupKFold
A cross-validation splitting method in scikit-learn that partitions data by group variable while maintaining label distribution through stratification, preventing data from the same group from appearing in both the training and test sets.
Timeline (last 90 days)
If the UCI diabetes readmission dataset is split naively at random, 6,791 patients appear in both the training and test sets, and 41.6% of the visits in the test set come from patients the model has already seen.
StratifiedGroupKFold is a splitter in scikit-learn for grouped cross-validation that keeps the target-variable distribution of each fold while ensuring all samples of the same group (such as a patient) appear in only one fold.
All Facts (2)
If the UCI diabetes readmission dataset is split naively at random, 6,791 patients appear in both the training and test sets, and 41.6% of the visits in the test set come from patients the model has already seen.
50%UnverifiedStratifiedGroupKFold is a splitter in scikit-learn for grouped cross-validation that keeps the target-variable distribution of each fold while ensuring all samples of the same group (such as a patient) appear in only one fold.
50%