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.

$2\mathrm{.}1\mathrm{.}4\mathrm{.}1$ 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.

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 metrics.

$"""$

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

# Usage example :

# accuracy, precision, recall, true$\_$positives, true$\_$negatives $=$ evaluate$\_$knn$\_$classifier$($X$\_$train, y$\_$train, X$\_$test, y$\_$test, best$\_$k$)$

# print$($f$"$Accuracy: $\{$accuracy$\},$ Precision: $\{$precision$\},$ Recall: $\{$recall$\},$ True Positives: $\{$true$\_$positives$\},$ True Negatives: $\{$true$\_$negatives$\}")$