Assignment: Histogram Classifier

📅 Deadline: 20.11.2024 21:59

🏦 Points: 4

In this assignment, you are tasked with training a histogram classifier, computing bounds on its generalization error, and computing bounds on its estimation error. You can find the complete description of the assignment in the Assignment PDF.

You are provided with a template containing the following files:

• main.py: This file includes the functions learn_classifier, generalization_bound, estimation_error_bound that you are required to implement.
• utils.py: Contains helper functions for loading and saving data. You do not need to modify this file.
• test-cases: A folder containing public test cases to help you verify your implementation before submitting to BRUTE.

Your objective is to implement the functions learn_classifier, generalization_bound, estimation_error_bound in main.py.

After completing your implementation, you can test your solution using the following commands before submitting it to BRUTE:

python main.py test-cases/public/instances/instance_1.json --plot

Expected output:

The trained histogram classifier achieves true error of at most 0.833 with probability at least 0.95
The trained histogram classifier achieves true error that differs from the best histogram classifier by at most 0.965 with probability at least 0.95
Comparing with reference solution
learn_classifier: 		Test OK
generalization_bound: 		Test OK
estimation_error_bound: 	Test OK

python main.py test-cases/public/instances/instance_2.json --plot

Expected output:

The trained histogram classifier achieves true error of at most 0.484 with probability at least 0.9
The trained histogram classifier achieves true error that differs from the best histogram classifier by at most 0.276 with probability at least 0.9
Comparing with reference solution
learn_classifier: 		Test OK
generalization_bound: 		Test OK
estimation_error_bound: 	Test OK

python main.py test-cases/public/instances/instance_3.json --plot

Expected output:

The trained histogram classifier achieves true error of at most 1.828 with probability at least 0.9
The trained histogram classifier achieves true error that differs from the best histogram classifier by at most 1.898 with probability at least 0.9
Comparing with reference solution
learn_classifier: 		Test OK
generalization_bound: 		Test OK
estimation_error_bound: 	Test OK

python main.py test-cases/public/instances/instance_4.json --plot

Expected output:

The trained histogram classifier achieves true error of at most 0.65 with probability at least 0.9
The trained histogram classifier achieves true error that differs from the best histogram classifier by at most 0.397 with probability at least 0.9
Comparing with reference solution
learn_classifier: 		Test OK
generalization_bound: 		Test OK
estimation_error_bound: 	Test OK

You may encounter issues with numerical errors in this test case. You should find a way to avoid them.

• Submit the completed code as a .zip via BRUTE.
• All python files must be stored in the root of the .zip sent for submission.
• Make sure your implementation passes the test cases provided above. Good luck! 😊