gemseo.mlearning.regression.algos.mlp_settings module#

Settings of the multiLayer perceptron (MLP).

Settings MLPRegressor_Settings(*, transformer=None, parameters=None, input_names=(), output_names=(), hidden_layer_sizes=(100,), random_state=0)[source]#

Bases: BaseRegressorSettings

The settings of the multiLayer perceptron (MLP).

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

Parameters:
Return type:

None

hidden_layer_sizes: tuple[PositiveInt, ...] = (100,)#

The number of neurons per hidden layer.

random_state: NonNegativeInt | None = 0#

The random state parameter.

If None, use the global random state instance from numpy.random. Creating the model multiple times will produce different results. If int, use a new random number generator seeded by this integer. This will produce the same results.