Uses of Class
org.tribuo.Excuse

Packages that use Excuse
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides an interface to LibLinear-java for anomaly detection problems.
Provides simple baseline multiclass classifiers.
Provides an interface to LibLinear-java for classification problems.
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
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 an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
This package contains the abstract implementation of an external model trained by something outside of Tribuo.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
Provides implementations of binary relevance based multi-label classification algorithms.
Provides simple baseline regression predictors.
Provides an interface to liblinear for regression problems.
Provides an implementation of decision trees for regression problems.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Provides infrastructure for applying transformations to a Dataset.