APPLICATION OF SUPERVISED LEARNING
CLASSIFICATION IN MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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The splitting of data into folds may be governed by criteria such as ensuring that each fold has the same proportion of observations
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*it leads to higher variation in the testing model as we are testing against one data point. If the data point is an outlier it can lead to higher variation.
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both are correct
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none of the above
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Detailed explanation-1: -The stratified k fold cross-validation is an extension of the cross-validation technique used for classification problems. It maintains the same class ratio throughout the K folds as the ratio in the original dataset.
Detailed explanation-2: -You need to know what “KFold” and “Stratified” are first. KFold is a cross-validator that divides the dataset into k folds. Stratified is to ensure that each fold of dataset has the same proportion of observations with a given label.
Detailed explanation-3: -Divide the data set into five random groups of equal size. Use four groups for training and one group for testing. Calculate the classification accuracy. Repeat the procedure four more times, rotating the test set so that each group serves as a test set exactly once. More items •09-Jul-2019