Uses of Class
org.tribuo.Example

Packages that use Example
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides anomaly data generators used for demos and testing implementations.
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 a multiclass data generator used for testing implementations, along with several synthetic data generators for 2d binary classification problems to be used in demos or tutorials.
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 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.
Provides clustering data generators used for demos and testing implementations.
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 classes for processing columnar data and generating Examples.
Provides interfaces for converting text inputs into Features and Examples.
Provides implementations of text data processors.
Provides utility datasets which subsample or otherwise transform the wrapped dataset.
Simple data sources for ingesting or aggregating data.
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.
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.
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 feature extraction implementations which use ONNX models.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
Provides a linear algebra system used for numerical operations in Tribuo.
Provides implementations of binary relevance based multi-label classification algorithms.
Provides a multi-label data generator for testing implementations and a configurable data source suitable for demos and tests.
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 some example regression data generators for testing implementations.
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 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.