Uses of Package
org.tribuo.classification

Packages that use org.tribuo.classification
Package
Description
Provides classes and infrastructure for multiclass classification problems.
Provides simple baseline multiclass classifiers.
Provides implementations of decision trees for classification problems.
Provides internal implementation classes for classification decision trees.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Evaluation classes for multi-class classification.
Provides a multiclass data generator used for testing implementations.
Provides a set of main methods for interacting with classification tasks.
Provides core infrastructure for local model based explanations.
Provides an implementation of LIME (Locally Interpretable Model Explanations).
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 classification sequence data generator for smoke testing implementations.
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 implementation of a classification linear model using Stochastic Gradient Descent.
Provides an interface to XGBoost for classification problems.
Provides a K-Nearest Neighbours implementation which works across all Tribuo Output types.
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 classes and infrastructure for working with multi-label classification problems.
Provides an implementation of independent multi-label classification that wraps a Label Trainer and uses it to make independent predictions of each label.