CI

evaluatio.inference.ci¶

Bootstrap-based confidence interval estimation utilities.

This module provides functionality to estimate empirical confidence intervals using bootstrap resampling. It also defines a lightweight container for returning confidence interval results.

The implementation relies on optimized native bindings for performance.

Functions¶

bootstrap_confidence_interval¶

bootstrap_confidence_interval(x: Iterable[float], iterations: int, alpha: float) -> ConfidenceInterval

Estimate a confidence interval using bootstrap resampling

Parameters

• x : Iterable[float]
  Input sample values.

• iterations : int
  Number of bootstrap resampling iterations.

• alpha : float
  Significance level for the confidence interval. For example, alpha=0.05 corresponds to a 95% confidence interval.

Returns

• ConfidenceInterval
  Results dataclass. Fields: mean (float with the mean of x), lower (float, lower CI bound), upper (float, upper CI bound). See ConfidenceInterval for full documentation.

Raises

• ValueError
  If iterations < 1, if x is empty, or if alpha is not in (0, 1).

Note

• The confidence interval is computed empirically via bootstrap sampling.

• The returned mean is computed from the original sample, not the bootstrap distribution.

Examples

>>> from evaluatio.inference.ci import bootstrap_confidence_interval
>>> values = [0.4, 0.7, 0.5, 0.9]
>>> result = bootstrap_confidence_interval(values, 1000, 0.05)
>>> result
ConfidenceInterval(mean=0.625, lower=0.45, upper=0.8)

Classes¶

ConfidenceInterval¶

Container for confidence interval results.

Attributes

• mean : float
  The mean of the input sample.

• lower : float
  The lower bound of the confidence interval.

• upper : float
  The upper bound of the confidence interval.