gemseo_mlearning / adaptive / criteria / mean_std

Show inherited members

criterion module

Combination of the expectation and standard deviation of the regression model.

Statistics:

\[E_sigma[x] = E[x] + \kappa \times sigma[x]\]

Estimator:

\[\widehat{E_sigma}[x] = \widehat{E}[x] + \kappa \times \widehat{sigma}[x]\]

class gemseo_mlearning.adaptive.criteria.mean_std.criterion.MeanSigma(algo_distribution, kappa)

Bases: MLDataAcquisitionCriterion

Combination of the expectation and standard deviation 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.

• kappa (float) – A factor associated with the standard deviation to increase or decrease the mean value.

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.

kappa: float

The factor associated with the standard deviation to increase the mean 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.