gemseo_mlearning / adaptive / criteria / quantile

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criterion module

Quantile of the regression model.

Statistics:

\[EI[x] = E[|q(\alpha)-Y(x)|]\]

where \(q\) is a quantile with level \(\alpha\).

Bootstrap estimator:

\[\widehat{EI}[x] = \frac{1}{B}\sum_{b=1}^B |q-Y_b(x)|\]

class gemseo_mlearning.adaptive.criteria.quantile.criterion.Quantile(algo_distribution, level)

Bases: LimitState

Expected Improvement of the regression model for a given quantile.

# noqa: D205 D212 D415

Parameters:

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

• level (float) – A quantile level.

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.

value: float

The value of interest.