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.

$"$n$"$

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

#$2$ Introduction to Support Vector Machines $($SVM$)$

Support Vector Machines $($SVM$)$ represent a powerful and versatile class of supervised machine learning algorithms, used for both classification and

regression tasks. At its core, SVM seeks to find the optimal separating hyperplane between different classes in the feature space. This hyperplane is chosen to

maximize the margin between the closest points of the classes, which are known as support vectors. This distinctive approach to classification enables SVM to

excel in a wide range of complex datasets, including those where the data points are not linearly separable.