Microsoft.ML.Trainers Namespace

Namespace containing trainers, model parameters, and utilities.

Classes

AveragedLinearOptions

Arguments class for averaged linear trainers.

AveragedLinearTrainer<TTransformer,TModel>

Base class for averaged linear trainers.

AveragedPerceptronTrainer

The IEstimator<TTransformer> to predict a target using a linear binary classification model trained with the averaged perceptron.

AveragedPerceptronTrainer.Options

Options for the AveragedPerceptronTrainer as used in AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options).

CoefficientStatistics

Represents a coefficient statistics object containing statistics about the calculated model parameters.

ComputeLogisticRegressionStandardDeviation

Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation, p-value and z-Score. Use this class' implementation in the Microsoft.ML.Mkl.Components package which uses Intel Math Kernel Library. Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.

ComputeLRTrainingStdThroughMkl
ExpLoss

Exponential Loss, commonly used in classification tasks.

ExponentialLRDecay

This class implements Exponential Learning rate decay. Implemented from the tensorflow documentation. Source: https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/exponential_decay Default values and implementation of learning rate is from Tensorflow Slim model tests. Source : https://github.com/tensorflow/models/blob/master/research/slim/train_image_classifier.py

FeatureContributionCalculator

Support for feature contribution calculation.

FieldAwareFactorizationMachineModelParameters

Model parameters for FieldAwareFactorizationMachineTrainer.

FieldAwareFactorizationMachinePredictionTransformer
FieldAwareFactorizationMachineTrainer

The IEstimator<TTransformer> to predict a target using a field-aware factorization machine model trained using a stochastic gradient method.

FieldAwareFactorizationMachineTrainer.Options

FieldAwareFactorizationMachineTrainer.Options for FieldAwareFactorizationMachineTrainer as used in FieldAwareFactorizationMachine(BinaryClassificationCatalog+BinaryClassificationTrainers, FieldAwareFactorizationMachineTrainer+Options).

HingeLoss

Hinge Loss, commonly used in classification tasks.

KMeansModelParameters
KMeansTrainer

The IEstimator<TTransformer> for training a KMeans clusterer

KMeansTrainer.Options

Options for the KMeansTrainer as used in KMeansTrainer(Options).

LbfgsLogisticRegressionBinaryTrainer

The IEstimator<TTransformer> to predict a target using a linear logistic regression model trained with L-BFGS method.

LbfgsLogisticRegressionBinaryTrainer.Options

Options for the LbfgsLogisticRegressionBinaryTrainer as used in LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

LbfgsMaximumEntropyMulticlassTrainer

The IEstimator<TTransformer> to predict a target using a maximum entropy multiclass classifier trained with L-BFGS method.

LbfgsMaximumEntropyMulticlassTrainer.Options

LbfgsMaximumEntropyMulticlassTrainer.Options for LbfgsMaximumEntropyMulticlassTrainer as used in LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LbfgsMaximumEntropyMulticlassTrainer+Options).

LbfgsPoissonRegressionTrainer

The IEstimator<TTransformer> for training a Poisson regression model.

LbfgsPoissonRegressionTrainer.Options

Options for the LbfgsPoissonRegressionTrainer as used in LbfgsPoissonRegression(Options).

LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase

Base options class for trainer estimators deriving fromLbfgsTrainerBase<TOptions,TTransformer,TModel>.

LbfgsTrainerBase<TOptions,TTransformer,TModel>

Base class for L-BFGS-based trainers.

LearningRateScheduler

This abstract class defines a learning rate scheduler.

LinearBinaryModelParameters

The model parameters class for linear binary trainer estimators.

LinearModelParameters

Base class for linear model parameters.

LinearModelParameterStatistics

Statistics for linear model parameters.

LinearMulticlassModelParameters

Linear model of multiclass classifiers. It outputs raw scores of all its linear models, and no probablistic output is provided.

LinearMulticlassModelParametersBase

Common linear model of multiclass classifiers. LinearMulticlassModelParameters contains a single linear model per class.

LinearRegressionModelParameters

Model parameters for linear regression.

LinearSvmTrainer

The IEstimator<TTransformer> to predict a target using a linear binary classification model trained with Linear SVM.

LinearSvmTrainer.Options

Options for the LinearSvmTrainer as used in LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options).

LinearTrainerBase<TTransformer,TModel>
LogLoss

The Log Loss, also known as the Cross Entropy Loss. It is commonly used in classification tasks.

LsrDecay

This class implements linear scaling rule and LR decay. Implementation adopted from RESNET-CIFAR benchmark test in Tensorflow slim. https://github.com/tensorflow/models/blob/b974c3f95a37acedcc3c58566834c78fcae4b214/official/vision/image_classification/resnet_cifar_main.py

MatrixFactorizationTrainer

The IEstimator<TTransformer> to predict elements in a matrix using matrix factorization (also known as a type of collaborative filtering).

MatrixFactorizationTrainer.Options

Options for the MatrixFactorizationTrainer as used in MatrixFactorization(Options).

MaximumEntropyModelParameters

Linear maximum entropy model of multiclass classifiers. It outputs classes probabilities. This model is also known as multinomial logistic regression. Please see https://en.wikipedia.org/wiki/Multinomial_logistic_regression for details.

MetaMulticlassTrainer<TTransformer,TModel>
ModelParametersBase<TOutput>

Generic base class for all model parameters.

ModelStatisticsBase

Statistics for linear model parameters.

NaiveBayesMulticlassModelParameters

Model parameters for NaiveBayesMulticlassTrainer.

NaiveBayesMulticlassTrainer

The IEstimator<TTransformer> for training a multiclass Naive Bayes model that supports binary feature values.

OlsModelParameters

Model parameters for OlsTrainer.

OlsTrainer

The IEstimator<TTransformer> for training a linear regression model using ordinary least squares (OLS) for estimating the parameters of the linear regression model.

OlsTrainer.Options

Options for the OlsTrainer as used in Ols(Options)

OneVersusAllModelParameters

Model parameters for OneVersusAllTrainer.

OneVersusAllTrainer

The IEstimator<TTransformer> for training a one-versus-all multi-class classifier that uses the specified binary classifier.

OnlineGradientDescentTrainer

The IEstimator<TTransformer> for training a linear regression model using Online Gradient Descent (OGD) for estimating the parameters of the linear regression model.

OnlineGradientDescentTrainer.Options

Options for the OnlineGradientDescentTrainer as used in OnlineGradientDescent(Options).

OnlineLinearOptions

Arguments class for online linear trainers.

OnlineLinearTrainer<TTransformer,TModel>

Base class for online linear trainers. Online trainers can be updated incrementally with additional data.

PairwiseCouplingModelParameters

Model parameters for PairwiseCouplingTrainer.

PairwiseCouplingTrainer

The IEstimator<TTransformer> for training a pairwise coupling multi-class classifier that uses the specified binary classifier.

PcaModelParameters

Model parameters for RandomizedPcaTrainer.

PoissonLoss

Poisson Loss function for Poisson Regression.

PoissonRegressionModelParameters

Model parameters for Poisson Regression.

PolynomialLRDecay

This class implements polynomial Learning rate decay. Implemented from the tensorflow documentation. Source: https://www.tensorflow.org/api_docs/python/tf/compat/v1/train/polynomial_decay Default values and implementation of learning rate is from Tensorflow Slim model tests. Source : https://github.com/tensorflow/models/blob/master/research/slim/train_image_classifier.py

PriorModelParameters

Model parameters for PriorTrainer.

PriorTrainer

The IEstimator<TTransformer> for predicting a target using a binary classification model.

RandomizedPcaTrainer

The IEstimator<TTransformer> for training an approximate PCA using Randomized SVD algorithm.

RandomizedPcaTrainer.Options

Options for the RandomizedPcaTrainer as used in RandomizedPca(Options).

RegressionModelParameters

Model parameters for regression.

SdcaBinaryTrainerBase<TModelParameters>.BinaryOptionsBase

Options for SdcaBinaryTrainerBase<TModelParameters>.

SdcaBinaryTrainerBase<TModelParameters>

SDCA is a general training algorithm for (generalized) linear models such as support vector machine, linear regression, logistic regression, and so on. SDCA binary classification trainer family includes several sealed members: (1) SdcaNonCalibratedBinaryTrainer supports general loss functions and returns LinearBinaryModelParameters. (2) SdcaLogisticRegressionBinaryTrainer essentially trains a regularized logistic regression model. Because logistic regression naturally provide probability output, this generated model's type is CalibratedModelParametersBase<TSubModel,TCalibrator>. where TSubModel is LinearBinaryModelParameters and TCalibrator is PlattCalibrator.

SdcaLogisticRegressionBinaryTrainer

The IEstimator<TTransformer> for training a binary logistic regression classification model using the stochastic dual coordinate ascent method. The trained model is calibrated and can produce probability by feeding the output value of the linear function to a PlattCalibrator.

SdcaLogisticRegressionBinaryTrainer.Options

Options for the SdcaLogisticRegressionBinaryTrainer as used in SdcaLogisticRegression(Options).

SdcaMaximumEntropyMulticlassTrainer

The IEstimator<TTransformer> to predict a target using a maximum entropy multiclass classifier. The trained model MaximumEntropyModelParameters produces probabilities of classes.

SdcaMaximumEntropyMulticlassTrainer.Options

SdcaMaximumEntropyMulticlassTrainer.Options for SdcaMaximumEntropyMulticlassTrainer as used in SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

SdcaMulticlassTrainerBase<TModel>.MulticlassOptions

Options for the SdcaMulticlassTrainerBase<TModel>.

SdcaMulticlassTrainerBase<TModel>

The IEstimator<TTransformer> to predict a target using a linear multiclass classifier model trained with a coordinate descent method. Depending on the used loss function, the trained model can be, for example, maximum entropy classifier or multi-class support vector machine.

SdcaNonCalibratedBinaryTrainer

The IEstimator<TTransformer> for training a binary logistic regression classification model using the stochastic dual coordinate ascent method.

SdcaNonCalibratedBinaryTrainer.Options

Options for the SdcaNonCalibratedBinaryTrainer.

SdcaNonCalibratedMulticlassTrainer

TheIEstimator<TTransformer> to predict a target using a linear multiclass classifier. The trained model LinearMulticlassModelParameters produces probabilities of classes.

SdcaNonCalibratedMulticlassTrainer.Options

SdcaNonCalibratedMulticlassTrainer.Options for SdcaNonCalibratedMulticlassTrainer as used in SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>).

SdcaRegressionTrainer

The IEstimator<TTransformer> for training a regression model using the stochastic dual coordinate ascent method.

SdcaRegressionTrainer.Options

Options for the SdcaRegressionTrainer.

SdcaTrainerBase<TOptions,TTransformer,TModel>.OptionsBase

Options for the SDCA-based trainers.

SdcaTrainerBase<TOptions,TTransformer,TModel>
SgdBinaryTrainerBase<TModel>.OptionsBase
SgdBinaryTrainerBase<TModel>
SgdCalibratedTrainer

The IEstimator<TTransformer> for training logistic regression using a parallel stochastic gradient method. The trained model is calibrated and can produce probability by feeding the output value of the linear function to a PlattCalibrator.

SgdCalibratedTrainer.Options

Options for the SgdCalibratedTrainer as used in SgdCalibrated(Options).

SgdNonCalibratedTrainer

The IEstimator<TTransformer> for training logistic regression using a parallel stochastic gradient method.

SgdNonCalibratedTrainer.Options

Options for the SgdNonCalibratedTrainer as used in SgdNonCalibrated(Options).

SmoothedHingeLoss

A smooth version of the HingeLoss function, commonly used in classification tasks.

SquaredLoss

The Squared Loss, commonly used in regression tasks.

StochasticTrainerBase<TTransformer,TModel>
SymbolicSgdLogisticRegressionBinaryTrainer

The IEstimator<TTransformer> to predict a target using a linear binary classification model trained with the symbolic stochastic gradient descent.

SymbolicSgdLogisticRegressionBinaryTrainer.Options

Options for the SymbolicSgdLogisticRegressionBinaryTrainer as used in SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options).

TrainerEstimatorBase<TTransformer,TModel>

This represents a basic class for 'simple trainer'. A 'simple trainer' accepts one feature column and one label column, also optionally a weight column. It produces a 'prediction transformer'.

TrainerEstimatorBaseWithGroupId<TTransformer,TModel>

This represents a basic class for 'simple trainer'. A 'simple trainer' accepts one feature column and one label column, also optionally a weight column. It produces a 'prediction transformer'.

TrainerInputBase

The base class for all trainer inputs.

TrainerInputBaseWithGroupId

The base class for all trainer inputs that support a group column.

TrainerInputBaseWithLabel

The base class for all trainer inputs that support a Label column.

TrainerInputBaseWithWeight

The base class for all trainer inputs that support a weight column.

TweedieLoss

Tweedie loss, based on the log-likelihood of the Tweedie distribution. This loss function is used in Tweedie regression.

UnsupervisedTrainerInputBaseWithWeight

The base class for all unsupervised trainer inputs that support a weight column.

Structs

LsrDecay.LearningRateSchedulerItem

This structure represents a learning rate scheduler item type

Interfaces

ICalculateFeatureContribution

Allows support for feature contribution calculation by model parameters.

IClassificationLoss
ILossFunction<TOutput,TLabel>
IRegressionLoss
IScalarLoss
ISupportSdcaClassificationLoss
ISupportSdcaLoss

The loss function may know the close-form solution to the optimal dual update Ref: Sec(6.2) of http://jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf

ISupportSdcaRegressionLoss
ITrainerEstimator<TTransformer,TModel>

Interface for the Trainer Estimator.

Enums

KMeansTrainer.InitializationAlgorithm
MatrixFactorizationTrainer.LossFunctionType

Type of loss function.