Requirements:

Implement a function named evaluate$\_$svm$\_$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.

SVM Kernel should be linear

Return:

Slope and intercept of the decision boundary.

Accuracy, precision, recall of the classifier on the test set.

Number of false positives and false negatives.

In $[]$: N def evaluate$\_$svm$\_$classifier$($X$\_$train, y$\_$train, X$\_$test, y$\_$test$)$:

$"""$

Trains an SVM classifier with a linear kernel on the training set and evaluates its performance on the test set.

Parameters:

X$\_$train: Training data features.

y$\_$train: Training data labels.

X$\_$test: Test data features.

y$\_$test: Test data labels.

Returns:

Slope and intercept of the decision boundary.

Accuracy, precision, recall on the test set.

Number of false positives and false negatives.

$"""$

return slope, intercept, accuracy, precision, recall, false$\_$positives, false$\_$negatives

# Usage example :

# slope, intercept, accuracy, precision, recall, false$\_$positives, false$\_$negatives $=$ evaluate$\_$svm$\_$classifier$($X$\_$train, y$\_$train,

# print$($f$"$Slope: $\{$slope$\},$ Intercept: $\{$intercept$\},$ Accuracy: $\{$accuracy$\},$ Precision: $\{$precision$\},$ Recall: $\{$recall$\},$ False Posit