Uses of Interface
org.tribuo.provenance.TrainerProvenance

Packages that use TrainerProvenance
Package
Description
Provides simple baseline multiclass classifiers.
Provides implementations of decision trees for classification problems.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.
Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
Provides an interface to XGBoost for classification problems.
Provides an implementation of HDBSCAN*.
Provides a multithreaded implementation of K-Means, with a configurable distance function.
Provides base classes for using liblinear from Tribuo.
The base interface to LibSVM.
Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
Provides the base classes for models trained with stochastic gradient descent.
Provides common functionality for building decision trees, irrespective of the predicted Output.
Provides abstract classes for interfacing with XGBoost abstracting away all the Output dependent parts.
Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
This package contains the abstract implementation of an external model trained by something outside of Tribuo.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
Provides an interface for working with TensorFlow sequence models, using Tribuo's SequenceModel abstraction.
Provides implementations of binary relevance based multi-label classification algorithms.
Provides a multi-label ensemble combiner that performs a (possibly weighted) majority vote among each label independently, along with an implementation of classifier chain ensembles.
Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
Provides internal implementations for empty provenance classes and TrainerProvenance.
Provides simple baseline regression predictors.
Provides an implementation of decision trees for regression problems.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Provides an interface to XGBoost for regression problems.
Provides core classes for working with sequences of Examples.
Provides infrastructure for applying transformations to a Dataset.