Implement a bootstrap confidence interval for win-rate from paired preference judgments

Hard

Evaluation: Benchmarks, Judges & Uncertainty3 / 4

Chapter 153 / 4

Evaluation: Benchmarks, Judges & Uncertainty

Implement a bootstrap confidence interval for win-rate from paired preference judgments

Hard

Target interface

bootstrap_winrate_ci(scores, n_boot=10000, alpha=0.05, seed=0)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.

solution.pyPython

Local draft

import numpy as np

def bootstrap_winrate_ci(scores, n_boot=10000, alpha=0.05, seed=0):
    raise NotImplementedError

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Chapter 153 / 4

Evaluation: Benchmarks, Judges & Uncertainty

Implement a bootstrap confidence interval for win-rate from paired preference judgments

Hard

Target interface

bootstrap_winrate_ci(scores, n_boot=10000, alpha=0.05, seed=0)

Implement the function/class skeleton in the editor. Any correct approach is accepted.

Hints0 / 2

Reference solution

Your own code stays in the editor.