10 Questions
Which type of cross-validation is used when the dataset contains only a small number of examples?
Leave one out cross-validation
What is the purpose of using k-fold cross-validation?
To obtain k values for accuracy
What is the advantage of using 2-fold cross-validation?
It uses large sets both for training and testing
What is the main difference between k-fold cross-validation and stratified cross-validation?
The distribution of labels in each fold
What is the final accuracy calculated in k-fold cross-validation?
The mean of the k values of accuracy obtained
The number of disjoint subsets in k-fold cross-validation is always equal to 5.
False
In 2-fold cross-validation, the classifier is built using the whole dataset.
False
In stratified cross-validation, each fold has a different distribution of labels.
False
Leave one out cross-validation is used when the dataset contains a large number of examples.
False
In k-fold cross-validation, the final accuracy is calculated by taking the median of the k values obtained.
False
Learn about different types of cross validation techniques, including k-fold and 2-fold cross validation, and how they are used to evaluate the accuracy of a classifier.
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