Uses of Interface
org.tribuo.ImmutableOutputInfo

Packages that use ImmutableOutputInfo
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides classes and infrastructure for anomaly detection problems.
Provides an interface to LibSVM for anomaly detection problems.
Provides classes and infrastructure for multiclass classification problems.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Evaluation classes for multi-class classification.
Provides an interface to LibLinear-java for classification problems.
Provides an interface to LibSVM for classification problems.
Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
Provides an interface to XGBoost for classification problems.
Provides classes and infrastructure for working with clustering problems.
Provides base classes for using liblinear from Tribuo.
The base interface to LibSVM.
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 utility datasets which subsample or otherwise transform the wrapped dataset.
Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
Provides implementations of base classes and interfaces from org.tribuo.
This package contains the abstract implementation of an external model trained by something outside of Tribuo.
This package contains a Tribuo wrapper around the ONNX Runtime.
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 classes and infrastructure for working with multi-label classification problems.
Evaluation classes for multi-label classification using MultiLabel.
Provides classes and infrastructure for regression problems with single or multiple output dimensions.
Provides EnsembleCombiner implementations for working with multi-output regression problems.
Evaluation classes for single or multi-dimensional regression.
Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
Provides an interface to liblinear for regression problems.
Provides an interface to LibSVM for regression problems.
Provides internal implementation classes for the regression trees.
Provides an interface to XGBoost for regression problems.
Provides core classes for working with sequences of Examples.