gemseo.mlearning.regression.algos.random_forest_settings module#

Settings of the multiLayer perceptron (MLP).

Settings RandomForestRegressor_Settings(*, transformer=<factory>, parameters=<factory>, input_names=(), output_names=(), n_estimators=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:

  • transformer (Mapping[str, Any]) --

    By default it is set to <factory>.

  • parameters (Mapping[str, Any]) --

    By default it is set to <factory>.

  • input_names (Sequence[str]) --

    By default it is set to ().

  • output_names (Sequence[str]) --

    By default it is set to ().

  • n_estimators (Annotated[int, Gt(gt=0)]) --

    By default it is set to 100.

  • random_state (Annotated[int, Ge(ge=0)] | None) --

    By default it is set to 0.

Return type:

None

n_estimators: PositiveInt = 100#

The number of trees in the forest.

Constraints:

  • gt = 0

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.

model_post_init(context, /)#

This function is meant to behave like a BaseModel method to initialise private attributes.

It takes context as an argument since that's what pydantic-core passes when calling it.

Parameters:

  • self (BaseModel) -- The BaseModel instance.

  • context (Any) -- The context.

Return type:

None