Uses of Interface
org.tribuo.WeightedExamples

Packages that use WeightedExamples
Package
Description
Provides implementations of decision trees for classification problems.
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
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.
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 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 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.