gemseo_mlearning / adaptive / criteria / variance

Show inherited members

criterion module

Variance of the regression model.

Statistics:

\[V[x] = E[(Y(x)-E[Y(x)])^2]\]

Bootstrap estimator:

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

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

class gemseo_mlearning.adaptive.criteria.variance.criterion.Variance(algo_distribution, **options)[source]

Bases: MLDataAcquisitionCriterion

Variance of the regression model.

This criterion is scaled by the output range.

Initialize self. See help(type(self)) for accurate signature.

Parameters:
  • 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.