cross-validation

we used k-fold cross-validation to evaluate the model's performance.

cross-validation helps prevent overfitting during model training.

the results from cross-validation were highly consistent across different folds.

we performed stratified cross-validation to maintain class proportions.

cross-validation provides a more reliable estimate of generalization error.

leave-one-out cross-validation is useful for small datasets.

we compared several algorithms using cross-validation as the evaluation metric.

cross-validation is a crucial step in machine learning model selection.

the cross-validation score was significantly higher than the baseline model.

we implemented nested cross-validation for robust hyperparameter tuning.

time-series data requires special considerations when using cross-validation.