COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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True
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False
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Either A or B
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None of the above
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Detailed explanation-1: -K-fold Cross-Validation is when the dataset is split into a K number of folds and is used to evaluate the model’s ability when given new data. K refers to the number of groups the data sample is split into. For example, if you see that the k-value is 5, we can call this a 5-fold cross-validation.
Detailed explanation-2: -Q22) Which of the following options is/are true for K-fold cross-validation? An increase in K will result in a higher time required to cross-validate the result. Higher values of K will result in higher confidence in the cross-validation result as compared to a lower value of K.
Detailed explanation-3: -Explanation. Cross-validation is a resampling technique for evaluating machine learning models on a small sample of data. The process includes only one parameter, k, which specifies the number of groups into which a given data sample should be divided.
Detailed explanation-4: -K-Folds Cross Validation: Because it ensures that every observation from the original dataset has the chance of appearing in training and test set. This is one among the best approach if we have a limited input data.