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LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LightGbmMulticlassTrainer+Options)
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Create LightGbmMulticlassTrainer with advanced options, which predicts a target using a gradient boosting decision tree multiclass classification model.
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LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers, Stream, String)
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Create LightGbmMulticlassTrainer from a pre-trained LightGBM model, which predicts a target using a gradient boosting decision tree multiclass classification model.
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LightGbm(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>,
Int32)
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Create LightGbmMulticlassTrainer, which predicts a target using a gradient boosting decision tree multiclass classification model.
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LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LbfgsMaximumEntropyMulticlassTrainer+Options)
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Create LbfgsMaximumEntropyMulticlassTrainer with advanced options, which predicts a target using a maximum entropy classification model trained with the L-BFGS method.
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LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)
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Create LbfgsMaximumEntropyMulticlassTrainer, which predicts a target using a maximum entropy classification model trained with the L-BFGS method.
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NaiveBayes(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String)
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Create a NaiveBayesMulticlassTrainer, which predicts a multiclass target using a Naive Bayes model
that supports binary feature values.
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OneVersusAll<TModel>(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
ITrainerEstimator<BinaryPredictionTransformer<TModel>,TModel>,
String, Boolean, IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>,
Int32, Boolean)
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Create a OneVersusAllTrainer, which predicts a multiclass target using one-versus-all strategy with
the binary classification estimator specified by binaryEstimator.
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PairwiseCoupling<TModel>(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
ITrainerEstimator<ISingleFeaturePredictionTransformer<TModel>,
TModel>, String, Boolean, IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>,
Int32)
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Create a PairwiseCouplingTrainer, which predicts a multiclass target using pairwise coupling strategy with
the binary classification estimator specified by binaryEstimator.
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SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaMaximumEntropyMulticlassTrainer+Options)
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Create SdcaMaximumEntropyMulticlassTrainer with advanced options, which predicts a target using a maximum entropy classification model trained with a coordinate descent method.
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SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)
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Create SdcaMaximumEntropyMulticlassTrainer, which predicts a target using a maximum entropy classification model trained with a coordinate descent method.
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SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaNonCalibratedMulticlassTrainer+Options)
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Create SdcaNonCalibratedMulticlassTrainer with advanced options, which predicts a target using a linear multiclass classification model trained with a coordinate descent method.
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SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>,
Nullable<Single>, Nullable<Int32>)
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Create SdcaNonCalibratedMulticlassTrainer, which predicts a target using a linear multiclass classification model trained with a coordinate descent method.
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NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, NerTrainer+NerOptions)
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Fine tune a Named Entity Recognition model.
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NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, Int32, Int32, BertArchitecture, IDataView)
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Fine tune a NAS-BERT model for Named Entity Recognition. The limit for any sentence is 512 tokens. Each word typically
will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token)
so in general this limit will be 510 words for all sentences.
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NameEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, NerTrainer+NerOptions)
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Obsolete.
Obsolete: please use the NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers, NerTrainer+NerOptions) method instead
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NameEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, Int32, Int32, BertArchitecture, IDataView)
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Obsolete.
Obsolete: please use the NamedEntityRecognition(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, Int32, Int32, BertArchitecture, IDataView) method instead
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ObjectDetection(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ObjectDetectionTrainer+Options)
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Fine tune an object detection model.
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ObjectDetection(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, String, String, Int32)
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Fine tune an object detection model.
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QuestionAnswer(MulticlassClassificationCatalog+MulticlassClassificationTrainers, QATrainer+Options)
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Fine tune a ROBERTA model for Question and Answer. The limit for any sentence is 512 tokens. Each word typically
will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token)
so in general this limit will be 510 words for all sentences.
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QuestionAnswer(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, String, String, String, Int32, Int32,
Int32, BertArchitecture, IDataView)
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Fine tune a ROBERTA model for Question and Answer. The limit for any sentence is 512 tokens. Each word typically
will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token)
so in general this limit will be 510 words for all sentences.
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TextClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, TextClassificationTrainer+TextClassificationOptions)
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Fine tune a NAS-BERT model for NLP classification. The limit for any sentence is 512 tokens. Each word typically
will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token)
so in general this limit will be 510 words for all sentences.
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TextClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers,
String, String, String, String, String, Int32, Int32, BertArchitecture,
IDataView)
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Fine tune a NAS-BERT model for NLP classification. The limit for any sentence is 512 tokens. Each word typically
will map to a single token, and we automatically add 2 specical tokens (a start token and a separator token)
so in general this limit will be 510 words for all sentences.
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ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ImageClassificationTrainer+Options)
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Create ImageClassificationTrainer using advanced options, which trains a Deep Neural Network(DNN) to classify images.
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ImageClassification(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, String, IDataView)
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Create ImageClassificationTrainer, which trains a Deep Neural Network(DNN) to classify images.
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