Why use k-fold cross validation on a small dataset?
Why A is the answer
For a steadier performance estimate
Each fold takes a turn as validation data, so the estimate rests on all the data.
In detail
- Each fold takes a turn as validation data.
- The estimate then rests on all the data.
- Each fold is validation once.
Use case
An illustrative scenario, not a real incident.
With only 500 samples, one split could be lucky. Five folds give a steadier estimate.
See it in different ways
flowchart LR A["Dataset"] --> B["Training: fit"] A --> C["Validation: tune"] A --> D["Test: final score"]
mindmap
root((ML basics))
Labeled data
Classification
Regression
Unlabeled data
Clustering
Validation
Cross validation
Held-out test setmindmap
root(("5-fold cross validation"))
n1["Round 1"]
n2["Fold 1 validates"]
n3["Round 2"]
n4["Fold 2 validates"]
n5["Round 5"]
n6["Fold 5 validates"]Key point: K-fold steadies the estimate.
Why the other options are wrong
- B. To create extra labeled examples for each of the classes
- It creates no new labels.
- C. To reduce the number of parameters in the final model
- It does not change parameters.
- D. To remove the need for a separate test of the model
- You still need a final test.