mqt.problemsolver.resource_estimation.error_budget_optimization.training

Module Contents

train(data: list[OrderedDict[str, float | int]]) → tuple[sklearn.ensemble.RandomForestRegressor, numpy.typing.NDArray[numpy.float64], numpy.typing.NDArray[numpy.float64]]

Trains a Random Forest Regressor on the provided data.

The function processes the input data to separate features and targets, splits the data into training and testing sets, and then trains a Random Forest Regressor on the training set.

Parameters:

data – Training data generated by generate_data().

Returns:

• The trained Random Forest Regressor.

• The testing set features.

• The testing set targets.