Which cross-validation technique is effective at minimizing bias in small datasets?

The Leave-one-out cross-validation technique is particularly effective at minimizing bias in small datasets because it uses almost all available data for training while still reserving one instance for testing. In a situation where the dataset is small, this approach allows the model to be trained on a large portion of the data, which maximizes the amount of information that the model can learn from. Each individual data point is used for validation exactly once, ensuring that the model has been evaluated with respect to every possible subset of the data.

This method contrasts with techniques like holdout, where a significant portion of data is not used for training at all, which can lead to bias, especially in small datasets where every single observation is crucial for training an effective model. Similarly, while stratified k-fold cross-validation and bagging can also help improve model performance and generalizability, they don't focus on minimizing bias in the same manner as leave-one-out does for small datasets. Thus, for scenarios with limited data, leave-one-out is favored for its thoroughness in validation while maintaining minimal data bias.