Uses of Class
org.tribuo.Prediction

Packages that use Prediction
Package
Description
Provides the core interfaces and classes for using Tribuo.
Evaluation classes for anomaly detection.
Provides an interface to LibLinear-java for anomaly detection problems.
Provides an interface to LibSVM for anomaly detection problems.
Provides simple baseline multiclass classifiers.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Evaluation classes for multi-class classification.
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 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 an implementation of a classification factorization machine 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.
Evaluation classes for clustering.
Provides an implementation of HDBSCAN*.
Provides a multithreaded implementation of K-Means, with a configurable distance function.
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.
Evaluation base classes, along with code for train/test splits and cross validation.
This package contains the infrastructure classes for building evaluation metrics.
This package contains the abstract implementation of an external model trained by something outside of Tribuo.
Code for uploading models to Oracle Cloud Infrastructure Data Science, and also for scoring models deployed in Oracle Cloud Infrastructure Data Science.
This package contains a Tribuo wrapper around 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 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.
Evaluation classes for multi-label classification using MultiLabel.
Provides an implementation of a multi-label classification factorization machine model using Stochastic Gradient Descent.
Provides an implementation of a multi-label classification linear model using Stochastic Gradient Descent.
Provides simple baseline regression predictors.
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 an implementation of decision trees for regression problems.
Provides an implementation of factorization machines for regression using Stochastic Gradient Descent.
Provides an implementation of linear regression using Stochastic Gradient Descent.
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.