Unsupervised cluster algorithms are implemented in mlr3cluster. Learners for survival analysis (or more general, for probabilistic regression) can be found in mlr3proba. More classification and regression learners are implemented in the add-on package mlr3learners. Predefined learners are stored in the dictionary mlr_learners, The fitted model stored in field $model, available after calling $train(). Meta-information about the requirements and capabilities of the learner. Or private methods $.train()/ $.predict()).Ī paradox::ParamSet which stores meta-information about available hyperparameters, and also stores hyperparameter settings. Methods $train() and $predict() which call internal methods (either public method $train_internal()/ $predict_internal() (deprecated) Learners are build around the three following key parts: This is the abstract base class for learner objects like LearnerClassif and LearnerRegr.
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