gemseo_mlearning / adaptive / criteria / standard_deviation

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criterion module

Standard deviation of the regression model.


\[\sigma[x] = \sqrt{E[(Y(x)-E[Y(x)])^2]}\]

Bootstrap estimator:

\[\hat{\sigma}[x] = \sqrt{\frac{1}{B-1}\sum_{b=1}^B (Y_b(x)-\widehat{E}[x])^2}\]

where \(\widehat{E}[x]= \frac{1}{B}\sum_{b=1}^B Y_b(x)\).

class gemseo_mlearning.adaptive.criteria.standard_deviation.criterion.StandardDeviation(algo_distribution, **options)[source]

Bases: MLDataAcquisitionCriterion

Standard Deviation of the regression model.

This criterion is scaled by the output range.

# noqa: D205 D212 D415

  • algo_distribution (MLRegressorDistribution) – The distribution of a machine learning algorithm.

  • **options (MLDataAcquisitionCriterionOptionType) – The acquisition criterion options.

algo_distribution: MLRegressorDistribution

The distribution of a machine learning algorithm assessor.

force_real: bool

Whether to cast the results to real value.

has_default_name: bool

Whether the name has been set with a default value.

last_eval: OutputType | None

The value of the function output at the last evaluation.

None if it has not yet been evaluated.

output_range: float

The output range.

special_repr: str

The string representation of the function overloading its default string ones.