twinlab.PredictParams

class twinlab.PredictParams(observation_noise=True, fidelity=None)

Parameter configuration for making predictions using a trained emulator.

Variables:

• observation_noise (bool) – Whether to include noise in the standard deviation of the prediction.

• data. (This latter uncertainty can potentially be improved by providing more training data, but may also be a limitation of the kernel used to describe the)

• trend. (If False, the noise term is excluded, outputting only the model uncertainty due to limitations in predicting the data)

• data.

• True. (The default value is)

• fidelity (Union[str, None], optional) – Fidelity information to be provided if the model is a multi-fidelity model (estimator_type="multi_fidelity_gp" in EstimatorParams). This must be the name of the column in the dataset that corresponds to the fidelity parameter. The default value is None, which is appropriate for most trained emulators.

__init__(observation_noise=True, fidelity=None)

Methods

__init__([observation_noise, fidelity])
unpack_parameters()