gemseo / mlearning / classification

Show inherited members

random_forest module

The random forest algorithm for classification.

The random forest classification model uses averaging methods on an ensemble of decision trees.

Dependence

The classifier relies on the RandomForestClassifier class of the scikit-learn library.

class gemseo.mlearning.classification.random_forest.RandomForestClassifier(data, transformer=mappingproxy({}), input_names=None, output_names=None, n_estimators=100, **parameters)[source]

Bases: MLClassificationAlgo

The random forest classification algorithm.

Parameters:

  • data (IODataset) – The learning dataset.

  • transformer (TransformerType) –

    The strategies to transform the variables. The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. "inputs" or "outputs" in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group. If IDENTITY, do not transform the variables.

    By default it is set to {}.

  • input_names (Iterable[str] | None) – The names of the input variables. If None, consider all the input variables of the learning dataset.

  • output_names (Iterable[str] | None) – The names of the output variables. If None, consider all the output variables of the learning dataset.

  • n_estimators (int) –

    The number of trees in the forest.

    By default it is set to 100.

  • **parameters (int | float | bool | str | None) – The parameters of the machine learning algorithm.

Raises:

ValueError – When both the variable and the group it belongs to have a transformer.

LIBRARY: Final[str] = 'scikit-learn'

The name of the library of the wrapped machine learning algorithm.

SHORT_ALGO_NAME: ClassVar[str] = 'RF'

The short name of the machine learning algorithm, often an acronym.

Typically used for composite names, e.g. f"{algo.SHORT_ALGO_NAME}_{dataset.name}" or f"{algo.SHORT_ALGO_NAME}_{discipline.name}".

algo: Any

The interfaced machine learning algorithm.

input_names: list[str]

The names of the input variables.

input_space_center: dict[str, ndarray]

The center of the input space.

learning_set: IODataset

The learning dataset.

n_classes: int

The number of classes.

output_names: list[str]

The names of the output variables.

parameters: dict[str, MLAlgoParameterType]

The parameters of the machine learning algorithm.

transformer: dict[str, Transformer]

The strategies to transform the variables, if any.

The values are instances of Transformer while the keys are the names of either the variables or the groups of variables, e.g. “inputs” or “outputs” in the case of the regression algorithms. If a group is specified, the Transformer will be applied to all the variables of this group.

Examples using RandomForestClassifier

Random forest classification

Random forest classification