Uses of Class
org.tribuo.Dataset

Packages that use Dataset
Package
Description
Provides the core interfaces and classes for using Tribuo.
Provides anomaly data generators used for demos and testing implementations.
Provides an interface to LibLinear-java for anomaly detection problems.
Provides an interface to LibSVM for anomaly detection problems.
Provides simple baseline multiclass classifiers.
Provides implementations of decision trees for classification problems.
Provides internal implementation classes for classification decision trees.
Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
Provides a multiclass data generator used for testing implementations, along with several synthetic data generators for 2d binary classification problems to be used in demos or tutorials.
Provides a set of main methods for interacting with classification tasks.
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 a SGD implementation of a Kernel SVM using the Pegasos algorithm.
Provides an interface to XGBoost for classification problems.
Provides clustering data generators used for demos and testing implementations.
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 which can load columnar data (using a RowProcessor) from a CSV (or other character delimited format) file.
Provides utility datasets which subsample or otherwise transform the wrapped dataset.
Simple data sources for ingesting or aggregating data.
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.
Provides an interface to TensorFlow, allowing the training of non-sequential models using any supported Tribuo output type.
Provides a linear algebra system used for numerical operations in Tribuo.
Provides implementations of binary relevance based multi-label classification algorithms.
Provides a multi-label ensemble combiner that performs a (possibly weighted) majority vote among each label independently, along with an implementation of classifier chain ensembles.
Provides a multi-label data generator for testing implementations and a configurable data source suitable for demos and tests.
Provides Tribuo specific infrastructure for the Provenance system which tracks models and datasets.
Provides simple baseline regression predictors.
Provides some example regression data generators for testing implementations.
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 internal implementation classes for the regression trees.
Provides implementations of sparse linear regression using various forms of regularisation penalty.
Provides an interface to XGBoost for regression problems.
Reproducibility utility based on Tribuo's provenance objects.
Provides core classes for working with sequences of Examples.
Provides infrastructure for applying transformations to a Dataset.