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Question 51 - DSA-C02 discussion

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Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?

A.
k-fold cross-validation
Answers
A.
k-fold cross-validation
B.
Leave-one-out cross-validation
Answers
B.
Leave-one-out cross-validation
C.
Holdout method
Answers
C.
Holdout method
D.
All of the above
Answers
D.
All of the above
Suggested answer: B

Explanation:

Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.

Cross validation

It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.

Leave-one-out cross validation

Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.

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asked 23/09/2024
rita whitfield
38 questions
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