Uses of Class
org.tribuo.ImmutableFeatureMap

Packages that use ImmutableFeatureMap
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides an interface to LibSVM for anomaly detection problems.
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 infrastructure for SequenceModels which emit Labels at each step of the sequence.
Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
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 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 utility datasets which subsample or otherwise transform the wrapped dataset.
Provides an interface for model prediction combinations, two base classes for ensemble models, a base class for ensemble excuses, and a Bagging implementation.
Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
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.
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 a linear algebra system used for numerical operations in Tribuo.
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 internal implementation classes for the regression trees.
Provides core classes for working with sequences of Examples.