You are solving a classification task. You must evaluate your model on a limited data sample by using k-fold cross validation. You start by configuring a k parameter as the number of splits. You…

The correct answer is C. k=5. In k-fold cross validation, k represents the number of splits (folds) the dataset is divided into. Valid values must be integers greater than 1. k=0 and k=0.5 are mathematically invalid (you cannot have zero or fractional folds). k=1 would mean the entire dataset is used as…

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Question

You are solving a classification task. You must evaluate your model on a limited data sample by using k-fold cross validation. You start by configuring a k parameter as the number of splits. You need to configure the k parameter for the cross-validation. Which value should you use?

Options

Ak=0.5
Bk=0
Ck=5
Dk=1

How the community answered

(14 responses)

A
7% (1)
C
93% (13)

Explanation

In k-fold cross validation, k represents the number of splits (folds) the dataset is divided into. Valid values must be integers greater than 1. k=0 and k=0.5 are mathematically invalid (you cannot have zero or fractional folds). k=1 would mean the entire dataset is used as both training and validation, which defeats the purpose. k=5 (5-fold CV) is the most widely accepted standard value in practice - it strikes a balance between computational cost and reliable model evaluation by training on 4 folds and validating on the remaining 1, rotating through all combinations.

Topics

#k-fold cross-validation#model evaluation#cross-validation configuration

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