mlrl.testbed_sklearn.experiments.problem_domain module¶

Provides classes for implementing different kinds of problem domains to be tacked via the scikit-learn framework.

class mlrl.testbed_sklearn.experiments.problem_domain.SkLearnClassificationProblem(base_learner: sklearn.base.BaseEstimator, prediction_type: PredictionType, predictor_factory: PredictorFactory, fit_kwargs: dict[str, Any] | None = None, predict_kwargs: dict[str, Any] | None = None)¶

Represents a classification problem to be tackled via the scikit-learn framework.

class mlrl.testbed_sklearn.experiments.problem_domain.SkLearnProblem(base_learner: sklearn.base.BaseEstimator, prediction_type: PredictionType, predictor_factory: PredictorFactory, fit_kwargs: dict[str, Any] | None = None, predict_kwargs: dict[str, Any] | None = None)¶

An abstract base class for all classes that represent a specific problem domain to be tackled via the scikit-learn framework.

class PredictorFactory¶

Bases: ABC

An abstract base class for all factories that allow to create instances of type Predictor.

abstractmethod create() → Predictor¶

Creates and returns a new object of type Predictor.

abstract property feature_dtype: Any¶: The data type to be used for features.

property learner_name: str¶: See mlrl.testbed.experiments.problem_domain.ProblemDomain.learner_name()

class mlrl.testbed_sklearn.experiments.problem_domain.SkLearnRegressionProblem(base_learner: sklearn.base.BaseEstimator, prediction_type: PredictionType, predictor_factory: PredictorFactory, fit_kwargs: dict[str, Any] | None = None, predict_kwargs: dict[str, Any] | None = None)¶

Represents a regression problem to be tackled via the scikit-learn framework.