Podcast
Questions and Answers
Which type of cross-validation is used when the dataset contains only a small number of examples?
Which type of cross-validation is used when the dataset contains only a small number of examples?
- Stratified cross-validation
- Leave one out cross-validation (correct)
- 2-fold cross-validation
- k-fold cross-validation
What is the purpose of using k-fold cross-validation?
What is the purpose of using k-fold cross-validation?
- To reduce the size of the training set
- To test the classifier on a single example
- To obtain k values for accuracy (correct)
- To obtain a single value for accuracy
What is the advantage of using 2-fold cross-validation?
What is the advantage of using 2-fold cross-validation?
- It uses large sets both for training and testing (correct)
- It uses a smaller test set
- It is used for small datasets
- It uses a smaller training set
What is the main difference between k-fold cross-validation and stratified cross-validation?
What is the main difference between k-fold cross-validation and stratified cross-validation?
What is the final accuracy calculated in k-fold cross-validation?
What is the final accuracy calculated in k-fold cross-validation?
The number of disjoint subsets in k-fold cross-validation is always equal to 5.
The number of disjoint subsets in k-fold cross-validation is always equal to 5.
In 2-fold cross-validation, the classifier is built using the whole dataset.
In 2-fold cross-validation, the classifier is built using the whole dataset.
In stratified cross-validation, each fold has a different distribution of labels.
In stratified cross-validation, each fold has a different distribution of labels.
Leave one out cross-validation is used when the dataset contains a large number of examples.
Leave one out cross-validation is used when the dataset contains a large number of examples.
In k-fold cross-validation, the final accuracy is calculated by taking the median of the k values obtained.
In k-fold cross-validation, the final accuracy is calculated by taking the median of the k values obtained.
Flashcards are hidden until you start studying
