Uses of Class
org.tribuo.Model

Packages that use Model
Package
Description
Provides the core interfaces and classes for using Tribuo.
Evaluation classes for anomaly detection.
Provides an interface to LibLinear-java for anomaly detection problems.
Provides an interface to LibSVM for anomaly detection problems.
Provides classes and infrastructure for multiclass classification problems.
Provides simple baseline multiclass classifiers.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Evaluation classes for multi-class classification.
Provides a set of main methods for interacting with classification tasks.
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.
Evaluation classes for clustering.
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 loading in data from disk, processing it into examples, and splitting datasets for things like cross-validation and train-test splits.
Provides classes for processing columnar data and generating Examples.
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.
This package contains the infrastructure classes for building evaluation metrics.
Provides the base interface and implementations of the Model hashing which obscures the feature names stored in a model.
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.
Provides implementations of binary relevance based multi-label classification algorithms.
Evaluation classes for multi-label classification using MultiLabel.
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.
Evaluation classes for single or multi-dimensional regression.
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.
Reproducibility utility based on Tribuo's provenance objects.
Provides infrastructure for applying transformations to a Dataset.