Package | Description |
---|---|
org.tribuo |
Provides the core interfaces and classes for using Tribuo.
|
org.tribuo.anomaly.evaluation |
Evaluation classes for anomaly detection.
|
org.tribuo.anomaly.libsvm |
Provides an interface to LibSVM for anomaly detection problems.
|
org.tribuo.classification.baseline |
Provides simple baseline multiclass classifiers.
|
org.tribuo.classification.ensemble |
Provides majority vote ensemble combiners for classification
along with an implementation of multiclass Adaboost.
|
org.tribuo.classification.evaluation |
Evaluation classes for multi-class classification.
|
org.tribuo.classification.explanations |
Provides core infrastructure for local model based explanations.
|
org.tribuo.classification.explanations.lime |
Provides an implementation of LIME (Locally Interpretable Model Explanations).
|
org.tribuo.classification.liblinear |
Provides an interface to LibLinear-java for classification problems.
|
org.tribuo.classification.libsvm |
Provides an interface to LibSVM for classification problems.
|
org.tribuo.classification.mnb |
Provides an implementation of multinomial naive bayes (i.e., naive bayes for non-negative count data).
|
org.tribuo.classification.sequence |
Provides infrastructure for
SequenceModel s which
emit Label s at each step of the sequence. |
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. |
org.tribuo.classification.sgd.crf |
Provides an implementation of a linear chain CRF trained using Stochastic Gradient Descent.
|
org.tribuo.classification.sgd.kernel |
Provides a SGD implementation of a Kernel SVM using the Pegasos algorithm.
|
org.tribuo.classification.sgd.linear |
Provides an implementation of a classification linear model using Stochastic Gradient Descent.
|
org.tribuo.classification.xgboost |
Provides an interface to XGBoost for classification problems.
|
org.tribuo.clustering.evaluation |
Evaluation classes for clustering.
|
org.tribuo.clustering.kmeans |
Provides a multithreaded implementation of K-Means, with a
configurable distance function.
|
org.tribuo.common.nearest |
Provides a K-Nearest Neighbours implementation which works across
all Tribuo
Output types. |
org.tribuo.common.tree |
Provides common functionality for building decision trees, irrespective
of the predicted
Output . |
org.tribuo.common.xgboost |
Provides abstract classes for interfacing with XGBoost abstracting away all the
Output
dependent parts. |
org.tribuo.ensemble |
Provides an interface for model prediction combinations,
two base classes for ensemble models, a base class for
ensemble excuses, and a Bagging implementation.
|
org.tribuo.evaluation |
Evaluation base classes, along with code for train/test splits and cross validation.
|
org.tribuo.evaluation.metrics |
This package contains the infrastructure classes for building evaluation metrics.
|
org.tribuo.interop |
This package contains the abstract implementation of an external model
trained by something outside of Tribuo.
|
org.tribuo.interop.onnx |
This package contains a Tribuo wrapper around the ONNX Runtime.
|
org.tribuo.interop.tensorflow |
Provides an interface to Tensorflow, allowing the training of non-sequential models using any supported
Tribuo output type.
|
org.tribuo.interop.tensorflow.sequence |
Provides an interface for working with Tensorflow sequence models, using Tribuo's
SequenceModel abstraction. |
org.tribuo.multilabel.baseline | |
org.tribuo.multilabel.evaluation |
Evaluation classes for multi-label classification using
MultiLabel . |
org.tribuo.regression.baseline |
Provides simple baseline regression predictors.
|
org.tribuo.regression.ensemble |
Provides
EnsembleCombiner implementations
for working with multi-output regression problems. |
org.tribuo.regression.evaluation |
Evaluation classes for single or multi-dimensional regression.
|
org.tribuo.regression.impl | |
org.tribuo.regression.liblinear |
Provides an interface to liblinear for regression problems.
|
org.tribuo.regression.libsvm |
Provides an interface to LibSVM for regression problems.
|
org.tribuo.regression.rtree |
Provides an implementation of decision trees for regression problems.
|
org.tribuo.regression.sgd.linear |
Provides an implementation of linear regression using Stochastic Gradient Descent.
|
org.tribuo.regression.xgboost |
Provides an interface to XGBoost for regression problems.
|
org.tribuo.sequence |
Provides core classes for working with sequences of
Example s. |
org.tribuo.transform |
Provides infrastructure for applying transformations to a
Dataset . |
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
Excuse.getPrediction()
Returns the prediction being excused.
|
abstract Prediction<T> |
Model.predict(Example<T> example)
Uses the model to predict the output for a single example.
|
Modifier and Type | Method and Description |
---|---|
protected List<Prediction<T>> |
Model.innerPredict(Iterable<Example<T>> examples)
Called by the base implementations of
Model.predict(Iterable) and Model.predict(Dataset) . |
List<Prediction<T>> |
Model.predict(Dataset<T> examples)
Uses the model to predict the outputs for multiple examples contained in
a data set.
|
List<Prediction<T>> |
Model.predict(Iterable<Example<T>> examples)
Uses the model to predict the output for multiple examples.
|
Constructor and Description |
---|
Excuse(Example<T> example,
Prediction<T> prediction,
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> weights)
Constructs an excuse for the prediction of the supplied example, using the feature weights.
|
Prediction(Prediction<T> other,
int numUsed,
Example<T> example)
Constructs a prediction from the supplied arguments.
|
Modifier and Type | Method and Description |
---|---|
org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyMetric.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
protected org.tribuo.anomaly.evaluation.AnomalyMetric.Context |
AnomalyEvaluator.createContext(Model<Event> model,
List<Prediction<Event>> predictions) |
Modifier and Type | Method and Description |
---|---|
Prediction<Event> |
LibSVMAnomalyModel.predict(Example<Event> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
DummyClassifierModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions) |
Prediction<Label> |
FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions) |
Prediction<Label> |
VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions,
float[] weights) |
Prediction<Label> |
FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions,
float[] weights) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions) |
Prediction<Label> |
FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions) |
Prediction<Label> |
VotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions,
float[] weights) |
Prediction<Label> |
FullyWeightedVotingCombiner.combine(ImmutableOutputInfo<Label> outputInfo,
List<Prediction<Label>> predictions,
float[] weights) |
Modifier and Type | Method and Description |
---|---|
static double |
LabelMetrics.AUCROC(Label label,
List<Prediction<Label>> predictions)
Area under the ROC curve.
|
static double |
LabelMetrics.AUCROC(MetricTarget<Label> tgt,
List<Prediction<Label>> predictions)
Area under the ROC curve.
|
static double |
LabelMetrics.averagedPrecision(Label label,
List<Prediction<Label>> predictions) |
static double |
LabelMetrics.averagedPrecision(MetricTarget<Label> tgt,
List<Prediction<Label>> predictions) |
LabelMetric.Context |
LabelMetric.createContext(Model<Label> model,
List<Prediction<Label>> predictions) |
protected LabelMetric.Context |
LabelEvaluator.createContext(Model<Label> model,
List<Prediction<Label>> predictions) |
static LabelEvaluationUtil.PRCurve |
LabelMetrics.precisionRecallCurve(Label label,
List<Prediction<Label>> predictions) |
Constructor and Description |
---|
Context(Model<Label> model,
List<Prediction<Label>> predictions) |
Context(SequenceModel<Label> model,
List<Prediction<Label>> predictions) |
LabelConfusionMatrix(ImmutableOutputInfo<Label> domain,
List<Prediction<Label>> predictions)
Creates a confusion matrix from the supplied predictions and label info.
|
LabelConfusionMatrix(Model<Label> model,
List<Prediction<Label>> predictions)
Creates a confusion matrix from the supplied predictions, using the label info
from the supplied model.
|
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
Explanation.getPrediction()
The original model's prediction which is being explained.
|
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
LIMEExplanation.getPrediction() |
Modifier and Type | Method and Description |
---|---|
static Regressor |
LIMEBase.transformOutput(Prediction<Label> prediction)
Transforms a
Prediction for a multiclass problem into a Regressor
output which represents the probability for each class. |
Constructor and Description |
---|
LIMEExplanation(SparseModel<Regressor> model,
Prediction<Label> prediction,
RegressionEvaluation evaluation) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
LibLinearClassificationModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
LibSVMClassificationModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
MultinomialNaiveBayesModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<Label>> |
LabelSequenceEvaluation.getPredictions()
Gets the flattened predictions.
|
Modifier and Type | Method and Description |
---|---|
protected LabelMetric.Context |
LabelSequenceEvaluator.createContext(SequenceModel<Label> model,
List<List<Prediction<Label>>> predictions) |
static <SUB extends ConfidencePredictingSequenceModel.Subsequence> |
ConfidencePredictingSequenceModel.multiplyWeights(List<Prediction<Label>> predictions,
List<SUB> subsequences)
A scoring method which multiplies together the per prediction scores.
|
abstract <SUB extends ConfidencePredictingSequenceModel.Subsequence> |
ConfidencePredictingSequenceModel.scoreSubsequences(SequenceExample<Label> example,
List<Prediction<Label>> predictions,
List<SUB> subsequences)
The scoring function for the subsequences.
|
Modifier and Type | Method and Description |
---|---|
List<List<Prediction<Label>>> |
ViterbiModel.predict(SequenceDataset<Label> examples) |
List<Prediction<Label>> |
ViterbiModel.predict(SequenceExample<Label> examples) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<Label>> |
CRFModel.predict(SequenceExample<Label> example) |
Modifier and Type | Method and Description |
---|---|
<SUB extends ConfidencePredictingSequenceModel.Subsequence> |
CRFModel.scoreSubsequences(SequenceExample<Label> example,
List<Prediction<Label>> predictions,
List<SUB> subsequences) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
KernelSVMModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
LinearSGDModel.predict(Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Label> |
XGBoostClassificationConverter.convertOutput(ImmutableOutputInfo<Label> info,
List<float[]> probabilitiesList,
int numValidFeatures,
Example<Label> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<Label>> |
XGBoostClassificationConverter.convertBatchOutput(ImmutableOutputInfo<Label> info,
List<float[][]> probabilitiesList,
int[] numValidFeatures,
Example<Label>[] examples) |
Modifier and Type | Method and Description |
---|---|
org.tribuo.clustering.evaluation.ClusteringMetric.Context |
ClusteringMetric.createContext(Model<ClusterID> model,
List<Prediction<ClusterID>> predictions) |
protected org.tribuo.clustering.evaluation.ClusteringMetric.Context |
ClusteringEvaluator.createContext(Model<ClusterID> model,
List<Prediction<ClusterID>> predictions) |
Modifier and Type | Method and Description |
---|---|
Prediction<ClusterID> |
KMeansModel.predict(Example<ClusterID> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
KNNModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
protected List<Prediction<T>> |
KNNModel.innerPredict(Iterable<Example<T>> examples)
Uses the model to predict the output for multiple examples.
|
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
LeafNode.getPrediction(int numUsed,
Example<T> example)
Constructs a new prediction object based on this node's scores.
|
Prediction<T> |
TreeModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
protected Prediction<T> |
XGBoostExternalModel.convertOutput(float[][] output,
int numValidFeatures,
Example<T> example) |
Prediction<T> |
XGBoostOutputConverter.convertOutput(ImmutableOutputInfo<T> info,
List<float[]> probabilities,
int numValidFeatures,
Example<T> example)
Converts a list of float arrays from XGBoost Boosters into a Tribuo
Prediction . |
Prediction<T> |
XGBoostModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<T>> |
XGBoostOutputConverter.convertBatchOutput(ImmutableOutputInfo<T> info,
List<float[][]> probabilities,
int[] numValidFeatures,
Example<T>[] examples)
Converts a list of float arrays from XGBoost Boosters into a Tribuo
Prediction . |
protected List<Prediction<T>> |
XGBoostExternalModel.convertOutput(float[][] output,
int[] numValidFeatures,
List<Example<T>> examples) |
List<Prediction<T>> |
XGBoostModel.predict(Dataset<T> examples)
Uses the model to predict the labels for multiple examples contained in
a data set.
|
List<Prediction<T>> |
XGBoostModel.predict(Iterable<Example<T>> examples)
Uses the model to predict the label for multiple examples.
|
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
EnsembleCombiner.combine(ImmutableOutputInfo<T> outputInfo,
List<Prediction<T>> predictions)
Combine the predictions.
|
Prediction<T> |
EnsembleCombiner.combine(ImmutableOutputInfo<T> outputInfo,
List<Prediction<T>> predictions,
float[] weights)
Combine the supplied predictions.
|
Prediction<T> |
WeightedEnsembleModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
EnsembleCombiner.combine(ImmutableOutputInfo<T> outputInfo,
List<Prediction<T>> predictions)
Combine the predictions.
|
Prediction<T> |
EnsembleCombiner.combine(ImmutableOutputInfo<T> outputInfo,
List<Prediction<T>> predictions,
float[] weights)
Combine the supplied predictions.
|
Constructor and Description |
---|
EnsembleExcuse(Example<T> example,
Prediction<T> prediction,
Map<String,List<com.oracle.labs.mlrg.olcut.util.Pair<String,Double>>> weights,
List<Excuse<T>> innerExcuses) |
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
OnlineEvaluator.predictAndObserve(Example<T> example)
Feeds the example to the model, records the prediction and returns it.
|
Modifier and Type | Method and Description |
---|---|
List<Prediction<T>> |
Evaluation.getPredictions()
Gets the predictions stored in this evaluation.
|
List<Prediction<T>> |
OnlineEvaluator.predictAndObserve(Iterable<Example<T>> examples)
Feeds the examples to the model, records the predictions and returns them.
|
Modifier and Type | Method and Description |
---|---|
void |
OnlineEvaluator.observe(Prediction<T> newPrediction)
Records the supplied prediction.
|
Modifier and Type | Method and Description |
---|---|
protected abstract C |
AbstractEvaluator.createContext(Model<T> model,
List<Prediction<T>> predictions)
Create the context needed for evaluation.
|
E |
Evaluator.evaluate(Model<T> model,
List<Prediction<T>> predictions,
DataProvenance dataProvenance)
Evaluates the model performance using the supplied predictions, returning an immutable
Evaluation
of the appropriate type. |
E |
AbstractEvaluator.evaluate(Model<T> model,
List<Prediction<T>> predictions,
DataProvenance dataProvenance)
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
|
default E |
Evaluator.evaluate(Model<T> model,
List<Prediction<T>> predictions,
List<T> groundTruth,
DataProvenance dataProvenance)
Evaluates the model performance using the supplied predictions, returning an immutable
Evaluation
of the appropriate type. |
void |
OnlineEvaluator.observe(List<Prediction<T>> newPredictions)
Records all the supplied predictions.
|
static <T extends Output<T>,C extends MetricContext<T>> |
EvaluationAggregator.summarize(List<? extends EvaluationMetric<T,C>> metrics,
Model<T> model,
List<Prediction<T>> predictions)
Summarize model performance on dataset across several metrics.
|
Modifier and Type | Method and Description |
---|---|
List<Prediction<T>> |
MetricContext.getPredictions()
Gets the predictions used by this context.
|
Modifier and Type | Method and Description |
---|---|
C |
EvaluationMetric.createContext(Model<T> model,
List<Prediction<T>> predictions)
Creates the context this metric uses to compute it's value.
|
Constructor and Description |
---|
MetricContext(Model<T> model,
List<Prediction<T>> predictions) |
MetricContext(SequenceModel<T> model,
List<Prediction<T>> predictions) |
Modifier and Type | Method and Description |
---|---|
protected abstract Prediction<T> |
ExternalModel.convertOutput(V output,
int numValidFeatures,
Example<T> example)
Converts the output of the external model into a
Prediction . |
Prediction<T> |
ExternalModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
protected abstract List<Prediction<T>> |
ExternalModel.convertOutput(V output,
int[] numValidFeatures,
List<Example<T>> examples)
Converts the output of the external model into a list of
Prediction s. |
protected List<Prediction<T>> |
ExternalModel.innerPredict(Iterable<Example<T>> examples) |
Modifier and Type | Method and Description |
---|---|
protected Prediction<T> |
ONNXExternalModel.convertOutput(List<ai.onnxruntime.OnnxValue> output,
int numValidFeatures,
Example<T> example)
Converts a tensor into a prediction.
|
Prediction<Label> |
LabelTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor,
ImmutableOutputInfo<Label> outputIDInfo,
int numValidFeatures,
Example<Label> example) |
Prediction<Regressor> |
RegressorTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> tensor,
ImmutableOutputInfo<Regressor> outputIDInfo,
int numValidFeatures,
Example<Regressor> example) |
Prediction<T> |
OutputTransformer.transformToPrediction(List<ai.onnxruntime.OnnxValue> value,
ImmutableOutputInfo<T> outputIDInfo,
int numValidFeatures,
Example<T> example)
Converts a
OnnxValue into a Prediction . |
Modifier and Type | Method and Description |
---|---|
protected List<Prediction<T>> |
ONNXExternalModel.convertOutput(List<ai.onnxruntime.OnnxValue> output,
int[] numValidFeatures,
List<Example<T>> examples)
Converts a tensor into a prediction.
|
List<Prediction<Label>> |
LabelTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor,
ImmutableOutputInfo<Label> outputIDInfo,
int[] numValidFeatures,
List<Example<Label>> examples) |
List<Prediction<Regressor>> |
RegressorTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> tensor,
ImmutableOutputInfo<Regressor> outputIDInfo,
int[] numValidFeatures,
List<Example<Regressor>> examples) |
List<Prediction<T>> |
OutputTransformer.transformToBatchPrediction(List<ai.onnxruntime.OnnxValue> value,
ImmutableOutputInfo<T> outputIDInfo,
int[] numValidFeatures,
List<Example<T>> examples)
Converts a
OnnxValue containing multiple outputs into a list of Prediction s. |
Modifier and Type | Method and Description |
---|---|
protected Prediction<T> |
TensorflowExternalModel.convertOutput(org.tensorflow.Tensor<?> output,
int numValidFeatures,
Example<T> example)
Converts a tensor into a prediction.
|
Prediction<T> |
TensorflowModel.predict(Example<T> example) |
Prediction<T> |
TensorflowCheckpointModel.predict(Example<T> example) |
Prediction<Label> |
LabelTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<Label> outputIDInfo,
int numValidFeatures,
Example<Label> example) |
Prediction<Regressor> |
RegressorTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<Regressor> outputIDInfo,
int numValidFeatures,
Example<Regressor> example) |
Prediction<T> |
OutputTransformer.transformToPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<T> outputIDInfo,
int numValidFeatures,
Example<T> example)
Converts a
Tensor into a Prediction . |
Modifier and Type | Method and Description |
---|---|
protected List<Prediction<T>> |
TensorflowExternalModel.convertOutput(org.tensorflow.Tensor<?> output,
int[] numValidFeatures,
List<Example<T>> examples)
Converts a tensor into a prediction.
|
protected List<Prediction<T>> |
TensorflowModel.innerPredict(Iterable<Example<T>> examples) |
List<Prediction<Label>> |
LabelTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<Label> outputIDInfo,
int[] numValidFeatures,
List<Example<Label>> examples) |
List<Prediction<Regressor>> |
RegressorTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<Regressor> outputIDInfo,
int[] numValidFeatures,
List<Example<Regressor>> examples) |
List<Prediction<T>> |
OutputTransformer.transformToBatchPrediction(org.tensorflow.Tensor<?> tensor,
ImmutableOutputInfo<T> outputIDInfo,
int[] numValidFeatures,
List<Example<T>> examples)
Converts a
Tensor containing multiple outputs into a list of Prediction s. |
Modifier and Type | Method and Description |
---|---|
List<List<Prediction<T>>> |
SequenceOutputTransformer.decode(org.tensorflow.Tensor<?> outputs,
List<SequenceExample<T>> inputBatch,
ImmutableOutputInfo<T> labelMap)
Decode graph output tensors corresponding to a batch of input sequences.
|
List<Prediction<T>> |
SequenceOutputTransformer.decode(org.tensorflow.Tensor<?> output,
SequenceExample<T> input,
ImmutableOutputInfo<T> labelMap)
Decode a tensor of graph output into a list of predictions for the input sequence.
|
List<Prediction<T>> |
TensorflowSequenceModel.predict(SequenceExample<T> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<MultiLabel> |
IndependentMultiLabelModel.predict(Example<MultiLabel> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<MultiLabel>> |
MultiLabelEvaluationImpl.getPredictions() |
Modifier and Type | Method and Description |
---|---|
org.tribuo.multilabel.evaluation.MultiLabelMetric.Context |
MultiLabelMetric.createContext(Model<MultiLabel> model,
List<Prediction<MultiLabel>> predictions) |
protected org.tribuo.multilabel.evaluation.MultiLabelMetric.Context |
MultiLabelEvaluator.createContext(Model<MultiLabel> model,
List<Prediction<MultiLabel>> predictions) |
Constructor and Description |
---|
MultiLabelConfusionMatrix(Model<MultiLabel> model,
List<Prediction<MultiLabel>> predictions) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
DummyRegressionModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo,
List<Prediction<Regressor>> predictions) |
Prediction<Regressor> |
AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo,
List<Prediction<Regressor>> predictions,
float[] weights) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo,
List<Prediction<Regressor>> predictions) |
Prediction<Regressor> |
AveragingCombiner.combine(ImmutableOutputInfo<Regressor> outputInfo,
List<Prediction<Regressor>> predictions,
float[] weights) |
Modifier and Type | Method and Description |
---|---|
org.tribuo.regression.evaluation.RegressionMetric.Context |
RegressionMetric.createContext(Model<Regressor> model,
List<Prediction<Regressor>> predictions) |
protected org.tribuo.regression.evaluation.RegressionMetric.Context |
RegressionEvaluator.createContext(Model<Regressor> model,
List<Prediction<Regressor>> predictions) |
Constructor and Description |
---|
RegressionSufficientStatistics(ImmutableOutputInfo<Regressor> domain,
List<Prediction<Regressor>> predictions,
boolean useExampleWeights) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
SkeletalIndependentRegressionSparseModel.predict(Example<Regressor> example) |
Prediction<Regressor> |
SkeletalIndependentRegressionModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
LibLinearRegressionModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
LibSVMRegressionModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
IndependentRegressionTreeModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
LinearSGDModel.predict(Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
Prediction<Regressor> |
XGBoostRegressionConverter.convertOutput(ImmutableOutputInfo<Regressor> info,
List<float[]> probabilities,
int numValidFeatures,
Example<Regressor> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<Regressor>> |
XGBoostRegressionConverter.convertBatchOutput(ImmutableOutputInfo<Regressor> info,
List<float[][]> probabilities,
int[] numValidFeatures,
Example<Regressor>[] examples) |
Modifier and Type | Method and Description |
---|---|
List<List<Prediction<T>>> |
SequenceModel.predict(Iterable<SequenceExample<T>> examples)
Uses the model to predict the output for multiple examples.
|
List<List<Prediction<T>>> |
SequenceModel.predict(SequenceDataset<T> examples)
Uses the model to predict the labels for multiple examples contained in
a data set.
|
abstract List<Prediction<T>> |
SequenceModel.predict(SequenceExample<T> example)
Uses the model to predict the output for a single example.
|
Modifier and Type | Method and Description |
---|---|
protected abstract C |
AbstractSequenceEvaluator.createContext(SequenceModel<T> model,
List<List<Prediction<T>>> predictions)
Create the context needed for evaluation.
|
E |
SequenceEvaluator.evaluate(SequenceModel<T> model,
List<List<Prediction<T>>> predictions,
DataProvenance dataProvenance)
Evaluates the supplied model and predictions by aggregating the appropriate statistics.
|
E |
AbstractSequenceEvaluator.evaluate(SequenceModel<T> model,
List<List<Prediction<T>>> predictions,
DataProvenance dataProvenance)
Produces an evaluation for the supplied model and predictions by aggregating the appropriate statistics.
|
static <T extends Output<T>> |
SequenceModel.toMaxLabels(List<Prediction<T>> predictions) |
Modifier and Type | Method and Description |
---|---|
Prediction<T> |
TransformedModel.predict(Example<T> example) |
Modifier and Type | Method and Description |
---|---|
List<Prediction<T>> |
TransformedModel.predict(Dataset<T> examples) |
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