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.

The names of functions must not change, even the return function: $"$DO NOT CHANGE FALSE

POSITIVES AND FALSE NEGATIVES."

def evaluate$\_$svm$\_$classifier$($X$\_$train, y$\_$train, X$\_$test, y$\_$test$)$:

$mn$

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.

$"$n$"$

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

From the expert

dataset is missing

Dataset is passed as parameter.

No need. I will call your functions and pass the dataset as a parameter. Also you do not need to split the data $($to training and testing subsets$)$