Calculate Performance Metrics for a Classification Model

Task: Implement Performance Metrics Calculation

In this task, you are required to implement a function performance_metrics(actual, predicted) that computes various performance metrics for a binary classification problem. These metrics include:

• Confusion Matrix
• Accuracy
• F1 Score
• Specificity
• Negative Predictive Value

The function should take in two lists:

• actual: The actual class labels (1 for positive, 0 for negative).
• predicted: The predicted class labels from the model.

Output

The function should return a tuple containing:

1. confusion_matrix: A 2x2 matrix.
2. accuracy: A float representing the accuracy of the model.
3. f1_score: A float representing the F1 score of the model.
4. specificity: A float representing the specificity of the model.
5. negative_predictive_value: A float representing the negative predictive value.

Constraints

• All elements in the actual and predicted lists must be either 0 or 1.
• Both lists must have the same length.

def performance_metrics(actual: list[int], predicted: list[int]) -> tuple:
	# Implement your code here
	return confusion_matrix, round(accuracy, 3), round(f1, 3), round(specificity, 3), round(negativePredictive, 3)