Question

A data scientist is building a regression model on a small dataset of only 300 labeled records. A single 80/20 train/test split produces validation metrics that vary widely each time the seed changes, making it hard to trust the model's estimated performance. The scientist wants a more reliable estimate of generalization error before registering the model. Which change to the training/validation strategy best addresses this problem?

Accepted Answer

Use k-fold cross-validation (e.g., 5-fold) so every record is used for both training and validation across folds, then average the metrics

Answer

Increase the test split from 20% to 50% so the validation set is larger and more stable

Answer

Remove the random seed entirely so results reflect natural variability

Answer

Train on 100% of the data and report the training-set metric as the performance estimate