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 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, bes

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