Estimators

All estimators live in the pytrees package and follow the scikit-learn conventions: parameters are set in the constructor, fit returns the estimator, fitted attributes end with an underscore, and the estimators can be cloned, pickled and used inside Pipeline, GridSearchCV or cross_val_score.

Some behaviour is shared by all of them:

• Labels. y can hold any labels np.unique accepts (integers, strings, and so on). classes_ lists them, and predict returns them.
• Binary features. DL8.5, LGDT and DL85Cluster only accept 0 and 1 in X and raise a ValueError otherwise, both in fit and in predict. Use a Binarizer, a KBinsDiscretizer with one-hot encoding, or your own thresholds to prepare the data.
• The fitted tree. tree_ is a pytrees.tree.Tree with the same layout as scikit-learn’s. apply, decision_path and to_dot work on it for every estimator.
• Search results. train_error_ is the training error of the tree, status_ says why the search stopped, and statistics_ holds counters such as the search time (duration, in seconds) and the size of the cache.