Uses of Package
org.tribuo.math.la

Packages that use org.tribuo.math.la
Package
Description
Provides an implementation of LIME (Locally Interpretable Model Explanations).
Provides infrastructure for Stochastic Gradient Descent for classification problems.
Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
Provides classification loss functions for Stochastic Gradient Descent.
Provides a multithreaded implementation of K-Means, with a configurable distance function.
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.
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 the ONNX Runtime.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
Contains the implementation of Tribuo's math library, it's gradient descent optimisers, kernels and a set of math related utils.
Provides a Kernel interface for Mercer kernels, along with implementations of standard kernels.
Provides a linear algebra system used for numerical operations in Tribuo.
 
Provides implementations of StochasticGradientOptimiser.
Provides some utility tensors for use in gradient optimisers.
Provides math related util classes.
Provides classes and infrastructure for working with multi-label classification problems.
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 multi-label classification loss functions for Stochastic Gradient Descent.
Provides skeletal implementations of Regressor Trainer that can wrap a single dimension trainer/model and produce one prediction per dimension independently.
Provides infrastructure for Stochastic Gradient Descent based regression models.
Provides an implementation of factorization machines for regression using Stochastic Gradient Descent.
Provides an implementation of linear regression using Stochastic Gradient Descent.
Provides regression loss functions for Stochastic Gradient Descent.
Provides implementations of sparse linear regression using various forms of regularisation penalty.