Uses of Class
org.tribuo.provenance.ModelProvenance

Packages that use ModelProvenance
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides an interface to LibSVM for anomaly detection problems.
Provides simple baseline multiclass classifiers.
Provides an interface to LibLinear-java for classification problems.
Provides an interface to LibSVM for classification problems.
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
Provides an implementation of a classification linear model using Stochastic Gradient Descent.
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 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.
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 implementation of independent multi-label classification that wraps a Label Trainer and uses it to make independent predictions of each label.
Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
Provides simple baseline regression predictors.
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 an implementation of decision trees for regression problems.
Provides an implementation of linear regression using Stochastic Gradient Descent.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Provides core classes for working with sequences of Examples.
Provides infrastructure for applying transformations to a Dataset.