$3$

$((($ Do it in python $)))$

$1\mathrm{.}3$ Using the KNN Classifier for Classification

The K$-$Nearest Neighbors $($KNN$)$ classifier offers a straightforward approach to classification

tasks by leveraging the properties of nearby data points. This task involves using the KNN

classifier to predict the class of new instances based on the $\text{'}K\text{'}$ nearest neighbors and

evaluating its performance on a test set.

Objective:

Use the KNN classifier to make predictions on a test dataset.

Evaluate the classifier's performance using metrics such as accuracy, precision, recall, true

positives, and true negatives.

Requirements:

Implement a function named evaluate$\_$knn$\_$classifier $.$

Parameters:

X$\_$train : Training data features as a numpy array.

y$\_$train : Training data labels as a numpy array.

X$\_$test : Test data features as a numpy array.

y$\_$test : Test data labels as a numpy array.

best$\_$k : The optimal number of neighbors as an integer. This should be taken from

the output of last function.

Return:

The function should return the evaluation metrics for the test set: accuracy, precision,

recall, true positives, and true negatives.

In $[]$: def evaluate$\_$knn$\_$classifier$($x$\_$train, y$\_$train, x$\_$test, y$\_$test, best$\_$k$)$:

$"""$

Evaluates the KNN classifier on the test set with the given best $\text{'}K\text{'}$ value

Parameters:

X$\_$train: Training data features.

y$\_$train: Training data labels.

X$\_$test: Test data features.

y$\_$test: Test data labels.

best$\_$k: The optimal$/$best number of neighbors.

Returns:

accuracy, precision, recall, true$\_$positives, true$\_$negatives: Evaluation

$"""$

return accuracy, precision, recall, true$\_$positives, true$\_$negatives

# Usage example :

# accuracy, precision, recall, true$\_$positives, true$\_$negatives $=$ evaluate$\_$knn$\_$c

# print $(f^{\text{'}\text{'}}$ Accuracy: $\{$accuracy$\},$ Precision: $\{$precision$\},$ Recall: $\{$recall$\},$ True