The question given to us is:

#### Answer the following for 5-fold cross validation on a training data set of 45 tuples:

#### (i) How many rounds of learning will be performed?

#### (ii) State the size of the training and testing set for each round of learning.

#### (iii) How would the accuracy of the classifier be calculated in 5-fold cross validation?

Let us solve this step-by-step.

(i) In 5-fold cross validation, the entire dataset is divided into 5 equally sized folds. Each fold is used once as a test set while the remaining 4 folds form the training set. Therefore, there will be 5 rounds of learning.

(ii)

Given a dataset of 45 tuples:

Each fold will consist of 45/5=9 tuples.

In each round, the training set will consist of the data from 4 folds, and the testing set will consist of the data from the remaining fold.

So, for each round:

The training set will have 4×9=36 tuples.

The testing set will have 9 tuples.

(iii)

The accuracy of the classifier in 5-fold cross validation is calculated as follows:

**Perform 5 Rounds of Learning:**

In each round, train the classifier on the training set (36 tuples) and test it on the testing set (9 tuples).

Calculate the accuracy for each of the 5 rounds. The accuracy for a round is the proportion of correctly classified instances out of the total instances in the test set.

**Calculate the Average Accuracy:**

After obtaining the accuracy for each of the 5 rounds, compute the overall accuracy by taking the average of these 5 accuracy values.

Formally, if Ai is the accuracy in the i-th round, then the overall accuracy A is given by:

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