Objective:

Train an SVM classifier with a linear kernel on the training dataset.

Evaluate the classifier's performance on the test dataset.

Return the slope and intercept of the decision boundary, along with key performance metrics: accuracy, precision, recall, false positives, and false negatives.

$2\mathrm{.}1\mathrm{.}6$ 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.

Y

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$\_$t$$