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?
What is the purpose of using k-fold cross-validation?
What is the purpose of using k-fold cross-validation?
What is the advantage of using 2-fold cross-validation?
What is the advantage of using 2-fold cross-validation?
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?
Signup and view all the answers
What is the final accuracy calculated in k-fold cross-validation?
What is the final accuracy calculated in k-fold cross-validation?
Signup and view all the answers
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.
Signup and view all the answers
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.
Signup and view all the answers
In stratified cross-validation, each fold has a different distribution of labels.
In stratified cross-validation, each fold has a different distribution of labels.
Signup and view all the answers
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.
Signup and view all the answers
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.
Signup and view all the answers
