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📅 Deadline: 13.11.2024 21:59
🏦 Points: 2
In this assignment, you are tasked with computing a generalization bound for a learning algorithm that operates within a finite hypothesis space. You can find the complete description of the assignment in the Assignment PDF.
You are provided with a template containing the following files:
Your objective is to implement the function generalization_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
Expected output:
Given that we selected the predictor from a hypothesis space of 216 predictors, the true risk of the predictor is smaller than [0.36] with probability at least 0.95 Comparing with reference solution Test OK
python main.py test-cases/public/instances/instance_2.json
Given that we selected the predictor from a hypothesis space of 54 predictors, the true risk of the predictor is smaller than [0.30] with probability at least 0.98 Comparing with reference solution Test OK
python main.py test-cases/public/instances/instance_3.json
Given that we selected the predictor from a hypothesis space of 54 predictors, the true risk of the predictor is smaller than [0.28] with probability at least 0.88 Comparing with reference solution Test OK