All Classes and Interfaces
Class
Description
Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
Creates a data source using a 2d checkerboard of alternating classes.
A tag interface for multi-class and multi-label classification tasks.
Options for building a classification ensemble.
The type of ensemble.
An
Options that can produce a classification Trainer based on the
provided arguments.Defines methods that calculate classification performance, used for both multi-class and multi-label classification.
A data source for two concentric circles, one per class.
A Sequence model which can provide confidence predictions for subsequence predictions.
A range class used to define a subsequence of a SequenceExample.
A confusion matrix for
Classifiables.Static functions for computing classification metrics based on a
ConfusionMatrix.A label feature extractor that produces several kinds of label-based features.
The base class for the 2d binary classification data sources in
org.tribuo.classification.example.Provenance for
DemoLabelDataSource.A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).
A trainer for simple baseline classifiers.
Types of dummy classifier.
A combiner which performs a weighted or unweighted vote across the predicted labels.
A data source for two classes generated from separate Gaussians.
An
ImmutableOutputInfo object for Labels.A data source of two interleaved half circles.
An immutable multi-class classification label.
A confusion matrix for
Labels.Adds multi-class classification specific metrics to
ClassifierEvaluation.Static utility functions for calculating performance metrics on
Labels.Stores the Precision-Recall curve as three arrays: the precisions, the recalls,
and the thresholds associated with those values.
Stores the ROC curve as three arrays: the false positive rate, the true positive rate,
and the thresholds associated with those rates.
A factory for making Label related classes.
Provenance for
LabelFactory.A class for featurising labels from previous steps in Viterbi.
The base class for information about multi-class classification Labels.
Generates three example train and test datasets, used for unit testing.
The context for a
LabelMetric is a ConfusionMatrix.An enum of the default
LabelMetrics supported by the multi-class classification
evaluation package.A class that can be used to evaluate a sequence label classification model element wise on a given set of data.
A sequence evaluator for labels.
A mutable
LabelInfo.A data source of two interleaved half circles with some zero mean Gaussian noise applied to each point.
A label feature extractor that doesn't produce any label based features.
Build and run a sequence classifier on a generated or serialized dataset using the trainer specified in the configuration file.
Command line options.
A data generator for smoke testing sequence label models.
This class provides static methods used by the demo classes in each classification backend.
An implementation of a viterbi model.
Types of label score aggregation.
Builds a Viterbi model using the supplied
Trainer.Options for building a viterbi trainer.
Type of label features to include.
A combiner which performs a weighted or unweighted vote across the predicted labels.
Tag interface denoting the
Trainer can use label weights.