$4$

$((($ Do it in python $)))$

$2\mathrm{.}1$ Utilizing the SVM Classifier for Classification with a Linear

Kernel

You will focus on using an SVM with a linear kernel to classify data. The objective is to train the

SVM classifier using the training dataset and evaluate its performance on a test dataset.

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.

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 $[]$: 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 eval

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.

n$"$ n$"$

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

# Usage example :

# slope, intercept, accuracy, precision, recall, false$\_$positives, false$\_$negati

# print $($f$"$Slope: $\{$slope$\},$ Intercept: $\{$intercept$\},$ Accuracy: $\{$accuracy$\},$ Precis