Index (Classification-Core 4.2.2 API)

A B C D E F G H I L M N O P R S T U V W X
All Classes and Interfaces|All Packages|Constant Field Values|Serialized Form

A

accuracy() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The overall accuracy of the evaluation.
accuracy() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The accuracy.
accuracy(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: The per label accuracy of the evaluation.
accuracy(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the accuracy for this label.
accuracy(EvaluationMetric.Average, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy using the specified average type and confusion matrix.
accuracy(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the accuracy given this confusion matrix.
accuracy(T, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates a per label accuracy given this confusion matrix.
ACCURACY - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The accuracy.
ADABOOST - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates an AdaBoostTrainer.
AdaBoostTrainer - Class in org.tribuo.classification.ensemble: Implements Adaboost.SAMME one of the more popular algorithms for multiclass boosting.
AdaBoostTrainer(Trainer<Label>, int) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer and the specified number of boosting rounds.
AdaBoostTrainer(Trainer<Label>, int, long) - Constructor for class org.tribuo.classification.ensemble.AdaBoostTrainer: Constructs an adaboost trainer using the supplied weak learner trainer, the specified number of boosting rounds and the supplied seed.
ADD - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Adds the scores.
asMap() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
AUCROC - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The area under the receiver-operator curve (ROC).
AUCROC(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve.
AUCROC(Label, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
AUCROC(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Area under the ROC curve.
averageAUCROC(boolean) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Area under the ROC curve averaged across labels.
AVERAGED_PRECISION - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The averaged precision.
averagedPrecision(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
averagedPrecision(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Summarises a Precision-Recall Curve by taking the weighted mean of the precisions at a given threshold, where the weight is the recall achieved at that threshold.
averagedPrecision(Label, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics
averagedPrecision(MetricTarget<Label>, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics

B

BAGGING - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates a BaggingTrainer.
BALANCED_ERROR_RATE - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The balanced error rate, i.e., the mean of the per class recalls.
balancedErrorRate() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the balanced error rate, i.e., the mean of the per label recalls.
balancedErrorRate() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the balanced error rate.
balancedErrorRate(ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the balanced error rate, i.e., the mean of the recalls.
begin - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: The subsequence start index.
binaryAUCROC(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the area under the receiver operator characteristic curve, i.e., the AUC of the ROC curve.
binarySparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.
binarySparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets with sparse features and unknown features in the test data.

C

CheckerboardDataSource - Class in org.tribuo.classification.example: Creates a data source using a 2d checkerboard of alternating classes.
CheckerboardDataSource(int, long, int, double, double) - Constructor for class org.tribuo.classification.example.CheckerboardDataSource: Creates a checkboard with the required number of squares per dimension, where each feature value lies between min and max.
Classifiable<T extends Classifiable<T>> - Interface in org.tribuo.classification: A tag interface for multi-class and multi-label classification tasks.
ClassificationEnsembleOptions - Class in org.tribuo.classification.ensemble: Options for building a classification ensemble.
ClassificationEnsembleOptions() - Constructor for class org.tribuo.classification.ensemble.ClassificationEnsembleOptions
ClassificationEnsembleOptions.EnsembleType - Enum in org.tribuo.classification.ensemble: The type of ensemble.
ClassificationOptions<TRAINER extends Trainer<Label>> - Interface in org.tribuo.classification: An Options that can produce a classification Trainer based on the provided arguments.
ClassifierEvaluation<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation: Defines methods that calculate classification performance, used for both multi-class and multi-label classification.
clear() - Method in class org.tribuo.classification.MutableLabelInfo
combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.ensemble.VotingCombiner
combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
combine(ImmutableOutputInfo<Label>, List<Prediction<Label>>, float[]) - Method in class org.tribuo.classification.ensemble.VotingCombiner
compute(LabelMetric.Context) - Method in class org.tribuo.classification.evaluation.LabelMetric
ConcentricCirclesDataSource - Class in org.tribuo.classification.example: A data source for two concentric circles, one per class.
ConcentricCirclesDataSource(int, long, double, double) - Constructor for class org.tribuo.classification.example.ConcentricCirclesDataSource: Constructs a data source for two concentric circles, one per class.
ConfidencePredictingSequenceModel - Class in org.tribuo.classification.sequence: A Sequence model which can provide confidence predictions for subsequence predictions.
ConfidencePredictingSequenceModel(String, ModelProvenance, ImmutableFeatureMap, ImmutableOutputInfo<Label>) - Constructor for class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel: Constructs a ConfidencePredictingSequenceModel with the supplied parameters.
ConfidencePredictingSequenceModel.Subsequence - Class in org.tribuo.classification.sequence: A range class used to define a subsequence of a SequenceExample.
confusion(Label, Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
confusion(Label, Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Note: confusion is not stored in the underlying map, so it won't show up in aggregation.
confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of times label truth was predicted as label predicted.
confusion(T, T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of times the supplied predicted label was returned for the supplied true class.
ConfusionMatrix<T extends Classifiable<T>> - Interface in org.tribuo.classification.evaluation: A confusion matrix for Classifiables.
ConfusionMetrics - Class in org.tribuo.classification.evaluation: Static functions for computing classification metrics based on a ConfusionMatrix.
CONSTANT - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns the supplied label for all inputs.
constructInfoForExternalModel(Map<Label, Integer>) - Method in class org.tribuo.classification.LabelFactory
Context(Model<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context: Constructs a context and compute the confusion matrix using the specified model and predictions.
Context(SequenceModel<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric.Context: Constructs a context and compute the confusion matrix using the specified model and predictions.
copy() - Method in class org.tribuo.classification.ImmutableLabelInfo
copy() - Method in class org.tribuo.classification.Label
copy() - Method in class org.tribuo.classification.LabelInfo
copy() - Method in class org.tribuo.classification.MutableLabelInfo
copy(String, ModelProvenance) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
createConstantTrainer(String) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Creates a trainer which creates models which return a fixed label.
createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
createContext(Model<Label>, List<Prediction<Label>>) - Method in class org.tribuo.classification.evaluation.LabelMetric
createContext(SequenceModel<Label>, List<List<Prediction<Label>>>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
createEvaluation(LabelMetric.Context, Map<MetricID<Label>, Double>, EvaluationProvenance) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
createMetrics(Model<Label>) - Method in class org.tribuo.classification.evaluation.LabelEvaluator
createMetrics(SequenceModel<Label>) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluator
createMostFrequentTrainer() - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Creates a trainer which creates models which return a fixed label, the one which was most frequent in the training data.
createStratifiedTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Creates a trainer which creates models which return random labels sampled from the training label distribution.
createUniformTrainer(long) - Static method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Creates a trainer which creates models which return random labels sampled uniformly from the labels seen at training time.

D

datasetName - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions: Name of the example dataset, options are {gorilla}.
DEFAULT - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: The default label features.
DefaultFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi: A label feature extractor that produces several kinds of label-based features.
DefaultFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor: Constructs a default feature extractor for bigrams and trigrams using the past 3 outcomes.
DefaultFeatureExtractor(int, int, boolean, boolean, boolean) - Constructor for class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor: Constructs a default feature extractor using the supplied parameters.
DemoLabelDataSource - Class in org.tribuo.classification.example: The base class for the 2d binary classification data sources in org.tribuo.classification.example.
DemoLabelDataSource.DemoLabelDataSourceProvenance - Class in org.tribuo.classification.example: Provenance for DemoLabelDataSource.
DemoLabelDataSourceProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.classification.example.DemoLabelDataSource.DemoLabelDataSourceProvenance: Constructs a provenance from the marshalled form.
denseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
denseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a train/test dataset pair which is dense in the features, each example has 4 features,{A,B,C,D}, and there are 4 classes, {Foo,Bar,Baz,Quux}.
domainAndIDEquals(ImmutableOutputInfo<Label>) - Method in class org.tribuo.classification.ImmutableLabelInfo
DummyClassifierModel - Class in org.tribuo.classification.baseline: A model which performs dummy classifications (e.g., constant output, uniform sampled labels, stratified sampled labels).
DummyClassifierTrainer - Class in org.tribuo.classification.baseline: A trainer for simple baseline classifiers.
DummyClassifierTrainer.DummyType - Enum in org.tribuo.classification.baseline: Types of dummy classifier.

E

emptyExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates an example with no features.
end - Variable in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: The subsequence end index.
ensembleSize - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions: Number of base learners in the ensemble.
equals(Object) - Method in class org.tribuo.classification.evaluation.LabelMetric
equals(Object) - Method in class org.tribuo.classification.Label
equals(Object) - Method in class org.tribuo.classification.LabelFactory
equals(Object) - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
examples - Variable in class org.tribuo.classification.example.DemoLabelDataSource
exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner: Exports this voting combiner to ONNX.
exportCombiner(ONNXNode) - Method in class org.tribuo.classification.ensemble.VotingCombiner: Exports this voting combiner to ONNX.
exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner: Exports this voting combiner to ONNX.
exportCombiner(ONNXNode, T) - Method in class org.tribuo.classification.ensemble.VotingCombiner: Exports this voting combiner to ONNX
EXTRA_TREES - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates an ExtraTreesTrainer.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
extractFeatures(List<Label>, double) - Method in interface org.tribuo.classification.sequence.viterbi.LabelFeatureExtractor: Generates features based on the previously produced labels.
extractFeatures(List<Label>, double) - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor

F

f1(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the F_1 score.
f1(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The F1 for this label.
f1(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the F_1 score.
f1(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the F_1 score, i.e., the harmonic mean of the precision and recall.
F1 - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The F_1 score, i.e., the harmonic mean of the precision and the recall.
factory - Static variable in class org.tribuo.classification.example.DemoLabelDataSource
FIRST_CLASS - Static variable in class org.tribuo.classification.example.DemoLabelDataSource: The first class.
fn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged number of false negatives.
fn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of false negatives.
fn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the micro averaged false negative count.
fn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
fn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The false negative count for this label.
fn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of false negatives, possibly averaged depending on the metric target.
fn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of false negatives, i.e., the number of times the true label was incorrectly predicted as another label.
fn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of false negatives for the supplied label.
FN - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of false negatives.
forTarget(MetricTarget<Label>) - Method in enum org.tribuo.classification.evaluation.LabelMetrics: Gets the LabelMetric wrapped around the supplied MetricTarget.
fp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro average of the number of false positives across all the labels, i.e., the total number of false positives.
fp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of false positives.
fp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the micro averaged false positive count.
fp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
fp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The false positive count for this label.
fp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of false positives, possibly averaged depending on the metric target.
fp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of false positives, i.e., the number of times this label was predicted but it was not the true label..
fp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of false positives for the supplied label.
FP - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of false positives.
fpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC: The false positive rate at the corresponding threshold.
fscore(double, double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the Fscore.
fscore(MetricTarget<T>, ConfusionMatrix<T>, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Computes the Fscore.
fullEquals(Label) - Method in class org.tribuo.classification.Label
FullyWeightedVotingCombiner - Class in org.tribuo.classification.ensemble: A combiner which performs a weighted or unweighted vote across the predicted labels.
FullyWeightedVotingCombiner() - Constructor for class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner: Constructs a weighted voting combiner.

G

GaussianLabelDataSource - Class in org.tribuo.classification.example: A data source for two classes generated from separate Gaussians.
GaussianLabelDataSource(int, long, double[], double[], double[], double[]) - Constructor for class org.tribuo.classification.example.GaussianLabelDataSource: Constructs a data source which contains two classes where each class is sampled from a 2d Gaussian with the specified parameters.
generate() - Method in class org.tribuo.classification.example.CheckerboardDataSource
generate() - Method in class org.tribuo.classification.example.ConcentricCirclesDataSource
generate() - Method in class org.tribuo.classification.example.DemoLabelDataSource: Generates the examples using the configured fields.
generate() - Method in class org.tribuo.classification.example.GaussianLabelDataSource
generate() - Method in class org.tribuo.classification.example.InterlockingCrescentsDataSource
generate() - Method in class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource
generateEmptyExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example with no examples.
generateGorillaA() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
generateGorillaB() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: Generates a sequence example with a mixture of features and three labels "O", "Status" and "Monkey".
generateGorillaDataset(int) - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: Generates a simple dataset consisting of numCopies repeats of two sequences.
generateImmutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
generateInfo() - Method in class org.tribuo.classification.LabelFactory: Generates an empty MutableLabelInfo.
generateInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example with features that are unused by the training data.
generateMutableOutputInfo() - Method in class org.tribuo.classification.LabelInfo
generateOtherInvalidExample() - Static method in class org.tribuo.classification.sequence.example.SequenceDataGenerator: This generates a sequence example where the first example has no features.
generateOutput(V) - Method in class org.tribuo.classification.LabelFactory: Generates the Label string by calling toString on the input.
generatePRCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the Precision Recall curve for a single label.
generateROCCurve(boolean[], double[]) - Static method in class org.tribuo.classification.evaluation.LabelEvaluationUtil: Calculates the binary ROC for a single label.
getClassName() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
getCM() - Method in class org.tribuo.classification.evaluation.LabelMetric.Context: Gets the confusion matrix.
getConfusionMatrix() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the underlying confusion matrix.
getConfusionMatrix() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the confusion matrix backing this evaluation.
getDomain() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: Returns the classification domain that this confusion matrix operates over.
getDomain() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
getDomain() - Method in class org.tribuo.classification.ImmutableLabelInfo: Returns the set of possible Labels that this LabelInfo has seen.
getDomain() - Method in class org.tribuo.classification.LabelInfo: Returns the set of possible Labels that this LabelInfo has seen.
getEvaluator() - Method in class org.tribuo.classification.LabelFactory
getExcuse(Example<Label>) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
getID(Label) - Method in class org.tribuo.classification.ImmutableLabelInfo
getImpl() - Method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns the implementing function for this metric.
getInvocationCount() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
getInvocationCount() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
getInvocationCount() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
getLabel() - Method in class org.tribuo.classification.Label: Gets the name of this label.
getLabelCount(int) - Method in class org.tribuo.classification.ImmutableLabelInfo: Returns the number of times the supplied id was observed before this LabelInfo was frozen.
getLabelCount(String) - Method in class org.tribuo.classification.LabelInfo: Gets the count of the supplied label, or 0 if the label is unknown.
getLabelCount(Label) - Method in class org.tribuo.classification.LabelInfo: Gets the count of the supplied label, or 0 if the label is unknown.
getLabelOrder() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The label order this confusion matrix uses in toString.
getLabelOrder() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix: Gets the current label order.
getName() - Method in class org.tribuo.classification.evaluation.LabelMetric
getOptionsDescription() - Method in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions
getOutput(int) - Method in class org.tribuo.classification.ImmutableLabelInfo
getOutputFactory() - Method in class org.tribuo.classification.example.DemoLabelDataSource
getPredictions() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the flattened predictions.
getProvenance() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
getProvenance() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
getProvenance() - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
getProvenance() - Method in class org.tribuo.classification.ensemble.VotingCombiner
getProvenance() - Method in class org.tribuo.classification.example.DemoLabelDataSource
getProvenance() - Method in class org.tribuo.classification.LabelFactory
getProvenance() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
getProvenance() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
getScore() - Method in class org.tribuo.classification.Label: Get a real valued score for this label.
getScoreAggregation() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel: Gets the score aggregation function.
getSequenceTrainer(Trainer<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions: Creates a viterbi trainer wrapping the supplied label trainer.
getSerializableForm(boolean) - Method in class org.tribuo.classification.Label: Returns "labelName" or "labelName,score=labelScore".
getStackSize() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel: Gets the stack size of this model.
getTarget() - Method in class org.tribuo.classification.evaluation.LabelMetric
getTopFeatures(int) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
getTopFeatures(int) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTopLabels(Map<String, Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTopLabels(Map<String, Label>, int) - Static method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
getTotalObservations() - Method in class org.tribuo.classification.ImmutableLabelInfo
getTrainer() - Method in interface org.tribuo.classification.ClassificationOptions: Constructs the trainer based on the provided arguments.
getUnknownCount() - Method in class org.tribuo.classification.LabelInfo
getUnknownOutput() - Method in class org.tribuo.classification.LabelFactory

H

hashCode() - Method in class org.tribuo.classification.evaluation.LabelMetric
hashCode() - Method in class org.tribuo.classification.Label
hashCode() - Method in class org.tribuo.classification.LabelFactory
hashCode() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance

I

ImmutableLabelInfo - Class in org.tribuo.classification: An ImmutableOutputInfo object for Labels.
innerTrainer - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
InterlockingCrescentsDataSource - Class in org.tribuo.classification.example: A data source of two interleaved half circles.
InterlockingCrescentsDataSource(int) - Constructor for class org.tribuo.classification.example.InterlockingCrescentsDataSource: Constructs an interlocking crescents data source.
invalidSparseExample() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates an example with the feature ids 1,5,8, which does not intersect with the ids used elsewhere in this class.
iterator() - Method in class org.tribuo.classification.example.DemoLabelDataSource
iterator() - Method in class org.tribuo.classification.ImmutableLabelInfo

L

label - Variable in class org.tribuo.classification.Label: The name of the label.
Label - Class in org.tribuo.classification: An immutable multi-class classification label.
Label(String) - Constructor for class org.tribuo.classification.Label: Builds a label with the sentinel score of Double.NaN.
Label(String, double) - Constructor for class org.tribuo.classification.Label: Builds a label with the supplied string and score.
LabelConfusionMatrix - Class in org.tribuo.classification.evaluation: A confusion matrix for Labels.
LabelConfusionMatrix(ImmutableOutputInfo<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelConfusionMatrix: Creates a confusion matrix from the supplied predictions and label info.
LabelConfusionMatrix(Model<Label>, List<Prediction<Label>>) - Constructor for class org.tribuo.classification.evaluation.LabelConfusionMatrix: Creates a confusion matrix from the supplied predictions, using the label info from the supplied model.
labelCounts - Variable in class org.tribuo.classification.LabelInfo: The occurrence counts of each label.
LabelEvaluation - Interface in org.tribuo.classification.evaluation: Adds multi-class classification specific metrics to ClassifierEvaluation.
LabelEvaluationUtil - Class in org.tribuo.classification.evaluation: Static utility functions for calculating performance metrics on Labels.
LabelEvaluationUtil.PRCurve - Class in org.tribuo.classification.evaluation: Stores the Precision-Recall curve as three arrays: the precisions, the recalls, and the thresholds associated with those values.
LabelEvaluationUtil.ROC - Class in org.tribuo.classification.evaluation: Stores the ROC curve as three arrays: the false positive rate, the true positive rate, and the thresholds associated with those rates.
LabelEvaluator - Class in org.tribuo.classification.evaluation: An Evaluator for Labels.
LabelEvaluator() - Constructor for class org.tribuo.classification.evaluation.LabelEvaluator
LabelFactory - Class in org.tribuo.classification: A factory for making Label related classes.
LabelFactory() - Constructor for class org.tribuo.classification.LabelFactory: Constructs a label factory.
LabelFactory.LabelFactoryProvenance - Class in org.tribuo.classification: Provenance for LabelFactory.
LabelFactoryProvenance(Map<String, Provenance>) - Constructor for class org.tribuo.classification.LabelFactory.LabelFactoryProvenance: Constructor used by the provenance serialization system.
LabelFeatureExtractor - Interface in org.tribuo.classification.sequence.viterbi: A class for featurising labels from previous steps in Viterbi.
LabelInfo - Class in org.tribuo.classification: The base class for information about multi-class classification Labels.
LabelledDataGenerator - Class in org.tribuo.classification.example: Generates three example train and test datasets, used for unit testing.
LabelMetric - Class in org.tribuo.classification.evaluation: A EvaluationMetric for Labels which calculates the value based on a ConfusionMatrix.
LabelMetric(MetricTarget<Label>, String, ToDoubleBiFunction<MetricTarget<Label>, LabelMetric.Context>) - Constructor for class org.tribuo.classification.evaluation.LabelMetric: Construct a new LabelMetric for the supplied metric target, using the supplied function.
LabelMetric.Context - Class in org.tribuo.classification.evaluation: The context for a LabelMetric is a ConfusionMatrix.
LabelMetrics - Enum in org.tribuo.classification.evaluation: An enum of the default LabelMetrics supported by the multi-class classification evaluation package.
labels - Variable in class org.tribuo.classification.LabelInfo: The label domain.
LabelSequenceEvaluation - Class in org.tribuo.classification.sequence: A class that can be used to evaluate a sequence label classification model element wise on a given set of data.
LabelSequenceEvaluation(Map<MetricID<Label>, Double>, LabelMetric.Context, EvaluationProvenance) - Constructor for class org.tribuo.classification.sequence.LabelSequenceEvaluation: Constructs a LabelSequenceEvaluation using the supplied parameters.
LabelSequenceEvaluator - Class in org.tribuo.classification.sequence: A sequence evaluator for labels.
LabelSequenceEvaluator() - Constructor for class org.tribuo.classification.sequence.LabelSequenceEvaluator
length() - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: Returns the number of elements in this subsequence.

M

macroAveragedF1() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged F_1 across all the labels.
macroAveragedF1() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The macro averaged F1.
macroAveragedPrecision() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged precision.
macroAveragedPrecision() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The macro averaged precision.
macroAveragedRecall() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged recall.
macroAveragedRecall() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The macro averaged recall.
macroFN() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of false negatives.
macroFN() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the macro averaged false negative count.
macroFP() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of false positives, averaged across the labels.
macroFP() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the macro averaged false positive count.
macroTN() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of true negatives.
macroTN() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the macro averaged true negative count.
macroTP() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the macro averaged number of true positives, averaged across the labels.
macroTP() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the macro averaged true positive count.
main(String[]) - Static method in class org.tribuo.classification.sequence.SeqTrainTest
microAveragedF1() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged F_1 across all labels.
microAveragedF1() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The micro averaged F1.
microAveragedPrecision() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged precision.
microAveragedPrecision() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The micro averaged precision.
microAveragedRecall() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro averaged recall.
microAveragedRecall() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The micro averaged recall.
MOST_FREQUENT - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns the most frequent training label.
MULTIPLY - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Multiplies the scores.
multiplyWeights(List<Prediction<Label>>, List<SUB>) - Static method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel: A scoring method which multiplies together the per prediction scores.
MutableLabelInfo - Class in org.tribuo.classification: A mutable LabelInfo.
MutableLabelInfo(LabelInfo) - Constructor for class org.tribuo.classification.MutableLabelInfo: Constructs a mutable deep copy of the supplied label info.

N

NoisyInterlockingCrescentsDataSource - Class in org.tribuo.classification.example: A data source of two interleaved half circles with some zero mean Gaussian noise applied to each point.
NoisyInterlockingCrescentsDataSource(int, long, double) - Constructor for class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource: Constructs a noisy interlocking crescents data source.
NONE - Enum constant in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: No label features.
NoopFeatureExtractor - Class in org.tribuo.classification.sequence.viterbi: A label feature extractor that doesn't produce any label based features.
NoopFeatureExtractor() - Constructor for class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
numMembers - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
numSamples - Variable in class org.tribuo.classification.example.DemoLabelDataSource

O

observe(Label) - Method in class org.tribuo.classification.MutableLabelInfo
observed() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The values this confusion matrix has seen.
observed() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
org.tribuo.classification - package org.tribuo.classification: Provides classes and infrastructure for multiclass classification problems.
org.tribuo.classification.baseline - package org.tribuo.classification.baseline: Provides simple baseline multiclass classifiers.
org.tribuo.classification.ensemble - package org.tribuo.classification.ensemble: Provides majority vote ensemble combiners for classification along with an implementation of multiclass Adaboost.
org.tribuo.classification.evaluation - package org.tribuo.classification.evaluation: Evaluation classes for multi-class classification.
org.tribuo.classification.example - package org.tribuo.classification.example: 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.
org.tribuo.classification.sequence - package org.tribuo.classification.sequence: Provides infrastructure for SequenceModels which emit Labels at each step of the sequence.
org.tribuo.classification.sequence.example - package org.tribuo.classification.sequence.example: Provides a classification sequence data generator for smoke testing implementations.
org.tribuo.classification.sequence.viterbi - package org.tribuo.classification.sequence.viterbi: Provides an implementation of Viterbi for generating structured outputs, which can sit on top of any Label based classification model.
outputCountsIterable() - Method in class org.tribuo.classification.LabelInfo
outputPath - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions: Path to serialize model to.

P

postConfig() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.example.CheckerboardDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.example.ConcentricCirclesDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.example.DemoLabelDataSource: Configures the class.
postConfig() - Method in class org.tribuo.classification.example.GaussianLabelDataSource: Used by the OLCUT configuration system, and should not be called by external code.
postConfig() - Method in class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource: Used by the OLCUT configuration system, and should not be called by external code.
PRCurve(double[], double[], double[]) - Constructor for class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve: Constructs a precision-recall curve.
precision - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve: The precision at the corresponding threshold.
precision(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the precision based upon the supplied statistics.
precision(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The precision for this label.
precision(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the precision for this metric target.
precision(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the precision of this label, i.e., the number of true positives divided by the number of true positives plus false positives.
PRECISION - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The precision, i.e., the number of true positives divided by the number of predicted positives.
precisionRecallCurve(Label) - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Calculates the Precision Recall curve for a single label.
precisionRecallCurve(Label, List<Prediction<Label>>) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics
predict(Example<Label>) - Method in class org.tribuo.classification.baseline.DummyClassifierModel
predict(SequenceDataset<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel
predict(SequenceExample<Label>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiModel

R

recall - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve: The recall at the corresponding threshold.
recall(double, double, double, double) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the recall based upon the supplied statistics.
recall(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The recall for this label.
recall(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Calculates the recall for this metric target.
recall(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the recall of this label, i.e., the number of true positives divided by the number of true positives plus false negatives.
RECALL - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The recall, i.e., the number of true positives divided by the number of ground truth positives.
RF - Enum constant in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Creates a RandomForestTrainer.
rng - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
rng - Variable in class org.tribuo.classification.example.DemoLabelDataSource
ROC(double[], double[], double[]) - Constructor for class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC: Constructs an ROC curve.
run(ConfigurationManager, DataOptions, Trainer<Label>) - Static method in class org.tribuo.classification.TrainTestHelper: This method trains a model on the specified training data, and evaluates it on the specified test data.

S

score - Variable in class org.tribuo.classification.Label: The score of the label.
scoreSubsequences(SequenceExample<Label>, List<Prediction<Label>>, List<SUB>) - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel: The scoring function for the subsequences.
SECOND_CLASS - Static variable in class org.tribuo.classification.example.DemoLabelDataSource: The second class.
seed - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
seed - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions: RNG seed.
seed - Variable in class org.tribuo.classification.example.DemoLabelDataSource
SeqTrainTest - Class in org.tribuo.classification.sequence: Build and run a sequence classifier on a generated or serialized dataset using the trainer specified in the configuration file.
SeqTrainTest() - Constructor for class org.tribuo.classification.sequence.SeqTrainTest
SeqTrainTest.SeqTrainTestOptions - Class in org.tribuo.classification.sequence: Command line options.
SeqTrainTestOptions() - Constructor for class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions
SequenceDataGenerator - Class in org.tribuo.classification.sequence.example: A data generator for smoke testing sequence label models.
setInvocationCount(int) - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
setInvocationCount(int) - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
setLabelOrder(List<Label>) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix: Sets the label order used in LabelConfusionMatrix.toString().
setLabelOrder(List<T>) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: Sets the label order this confusion matrix uses in toString.
setLabelWeights(Map<Label, Float>) - Method in interface org.tribuo.classification.WeightedLabels: Sets the label weights used by this trainer.
size() - Method in class org.tribuo.classification.LabelInfo: The number of unique Labels this LabelInfo has seen.
sparseTrainTest() - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
sparseTrainTest(double) - Static method in class org.tribuo.classification.example.LabelledDataGenerator: Generates a pair of datasets, where the features are sparse, and unknown features appear in the test data.
STRATIFIED - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Samples the label proprotional to the training label frequencies.
Subsequence(int, int) - Constructor for class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence: Constructs a subsequence for the fixed range, exclusive of the end.
sumOverOutputs(ImmutableOutputInfo<T>, ToDoubleFunction<T>) - Static method in interface org.tribuo.classification.evaluation.ConfusionMatrix: Sums the supplied getter over the domain.
support() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of examples this confusion matrix has seen.
support() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
support(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
support(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of examples with this true label this confusion matrix has seen.

T

testDataset - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions: Path to a serialised SequenceDataset used for testing.
thresholds - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.PRCurve: The threshold values.
thresholds - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC: The threshold values.
tn() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the total number of true negatives.
tn() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of true negatives.
tn() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the micro averaged true negative count.
tn(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
tn(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: The true negative count for this label.
tn(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of true negatives, possibly averaged depending on the metric target.
tn(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of true negatives for that label, i.e., the number of times it wasn't predicted, and was not the true label.
tn(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of true negatives for the supplied label.
TN - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of true negatives.
toFormattedString(LabelEvaluation) - Static method in interface org.tribuo.classification.evaluation.LabelEvaluation: This method produces a nicely formatted String output, with appropriate tabs and newlines, suitable for display on a terminal.
toHTML() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix: Emits a HTML table representation of the Confusion Matrix.
toHTML() - Method in interface org.tribuo.classification.evaluation.LabelEvaluation: Returns a HTML formatted String representing this evaluation.
toHTML(LabelEvaluation) - Static method in interface org.tribuo.classification.evaluation.LabelEvaluation: This method produces a HTML formatted String output, with appropriate tabs and newlines, suitable for integration into a webpage.
toReadableString() - Method in class org.tribuo.classification.ImmutableLabelInfo
toReadableString() - Method in class org.tribuo.classification.MutableLabelInfo
toString() - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
toString() - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
toString() - Method in class org.tribuo.classification.ensemble.FullyWeightedVotingCombiner
toString() - Method in class org.tribuo.classification.ensemble.VotingCombiner
toString() - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
toString() - Method in class org.tribuo.classification.evaluation.LabelMetric
toString() - Method in class org.tribuo.classification.example.CheckerboardDataSource
toString() - Method in class org.tribuo.classification.example.ConcentricCirclesDataSource
toString() - Method in class org.tribuo.classification.example.GaussianLabelDataSource
toString() - Method in class org.tribuo.classification.example.InterlockingCrescentsDataSource
toString() - Method in class org.tribuo.classification.example.NoisyInterlockingCrescentsDataSource
toString() - Method in class org.tribuo.classification.ImmutableLabelInfo
toString() - Method in class org.tribuo.classification.Label
toString() - Method in class org.tribuo.classification.LabelFactory.LabelFactoryProvenance
toString() - Method in class org.tribuo.classification.MutableLabelInfo
toString() - Method in class org.tribuo.classification.sequence.ConfidencePredictingSequenceModel.Subsequence
toString() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation
toString() - Method in class org.tribuo.classification.sequence.viterbi.DefaultFeatureExtractor
toString() - Method in class org.tribuo.classification.sequence.viterbi.NoopFeatureExtractor
toString() - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer
tp() - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the micro average of the number of true positives across all the labels, i.e., the total number of true positives.
tp() - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The total number of true positives.
tp() - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the micro averaged true positive count.
tp(Label) - Method in class org.tribuo.classification.evaluation.LabelConfusionMatrix
tp(Label) - Method in class org.tribuo.classification.sequence.LabelSequenceEvaluation: Gets the true positive count for that label.
tp(MetricTarget<T>, ConfusionMatrix<T>) - Static method in class org.tribuo.classification.evaluation.ConfusionMetrics: Returns the number of true positives, possibly averaged depending on the metric target.
tp(T) - Method in interface org.tribuo.classification.evaluation.ClassifierEvaluation: Returns the number of true positives, i.e., the number of times the label was correctly predicted.
tp(T) - Method in interface org.tribuo.classification.evaluation.ConfusionMatrix: The number of true positives for the supplied label.
TP - Enum constant in enum org.tribuo.classification.evaluation.LabelMetrics: The number of true positives.
tpr - Variable in class org.tribuo.classification.evaluation.LabelEvaluationUtil.ROC: The true positive rate at the corresponding threshold.
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
train(Dataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer: If the trainer implements WeightedExamples then do boosting by weighting, otherwise do boosting by sampling.
train(Dataset<Label>, Map<String, Provenance>, int) - Method in class org.tribuo.classification.baseline.DummyClassifierTrainer
train(Dataset<Label>, Map<String, Provenance>, int) - Method in class org.tribuo.classification.ensemble.AdaBoostTrainer
train(SequenceDataset<Label>, Map<String, Provenance>) - Method in class org.tribuo.classification.sequence.viterbi.ViterbiTrainer: The viterbi train method is unique because it delegates to a regular Model train method, but before it does, it adds features derived from preceding labels.
trainDataset - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions: Path to a serialised SequenceDataset used for training.
trainer - Variable in class org.tribuo.classification.sequence.SeqTrainTest.SeqTrainTestOptions: Name of the trainer in the configuration file.
trainInvocationCounter - Variable in class org.tribuo.classification.ensemble.AdaBoostTrainer
TrainTestHelper - Class in org.tribuo.classification: This class provides static methods used by the demo classes in each classification backend.
type - Variable in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions: Ensemble method, options are {ADABOOST, BAGGING, RF}.

U

UNIFORM - Enum constant in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Samples uniformly from the label domain.
UNKNOWN - Static variable in class org.tribuo.classification.Label: The name of the unknown label (i.e., an unlabelled output).
UNKNOWN_LABEL - Static variable in class org.tribuo.classification.LabelFactory: The singleton unknown label, used for unlablled examples.
unknownCount - Variable in class org.tribuo.classification.LabelInfo: The number of unknown labels this LabelInfo has seen.

V

valueOf(String) - Static method in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Returns the enum constant of this type with the specified name.
valueOf(String) - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: Returns the enum constant of this type with the specified name.
values() - Static method in enum org.tribuo.classification.baseline.DummyClassifierTrainer.DummyType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.ensemble.ClassificationEnsembleOptions.EnsembleType: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.evaluation.LabelMetrics: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiModel.ScoreAggregation: Returns an array containing the constants of this enum type, in the order they are declared.
values() - Static method in enum org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions.ViterbiLabelFeatures: Returns an array containing the constants of this enum type, in the order they are declared.
ViterbiModel - Class in org.tribuo.classification.sequence.viterbi: An implementation of a viterbi model.
ViterbiModel.ScoreAggregation - Enum in org.tribuo.classification.sequence.viterbi: Types of label score aggregation.
ViterbiTrainer - Class in org.tribuo.classification.sequence.viterbi: Builds a Viterbi model using the supplied Trainer.
ViterbiTrainer(Trainer<Label>, LabelFeatureExtractor, int, ViterbiModel.ScoreAggregation) - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainer: Constructs a ViterbiTrainer wrapping the specified trainer.
ViterbiTrainer(Trainer<Label>, LabelFeatureExtractor, ViterbiModel.ScoreAggregation) - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainer: Constructs a ViterbiTrainer wrapping the specified trainer, with an unbounded stack size.
ViterbiTrainerOptions - Class in org.tribuo.classification.sequence.viterbi: Options for building a viterbi trainer.
ViterbiTrainerOptions() - Constructor for class org.tribuo.classification.sequence.viterbi.ViterbiTrainerOptions
ViterbiTrainerOptions.ViterbiLabelFeatures - Enum in org.tribuo.classification.sequence.viterbi: Type of label features to include.
VotingCombiner - Class in org.tribuo.classification.ensemble: A combiner which performs a weighted or unweighted vote across the predicted labels.
VotingCombiner() - Constructor for class org.tribuo.classification.ensemble.VotingCombiner: Constructs a voting combiner.

W

WeightedLabels - Interface in org.tribuo.classification: Tag interface denoting the Trainer can use label weights.
wrapTrainer(Trainer<Label>) - Method in class org.tribuo.classification.ensemble.ClassificationEnsembleOptions: Wraps the supplied trainer using the ensemble trainer described by these options.

X

X1 - Static variable in class org.tribuo.classification.example.DemoLabelDataSource: The first feature name.
X2 - Static variable in class org.tribuo.classification.example.DemoLabelDataSource: The second feature name.

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