# Microsoft.ML.Trainers Namespace

Namespace containing trainers, model parameters, and utilities.

## Classes

 AveragedLinearOptions Arguments class for averaged linear trainers. AveragedLinearTrainer Base class for averaged linear trainers. AveragedPerceptronTrainer The IEstimator to predict a target using a linear binary classification model trained with the averaged perceptron. 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. FeatureContributionCalculator Support for feature contribution calculation. FieldAwareFactorizationMachineModelParameters Model parameters for FieldAwareFactorizationMachineTrainer. FieldAwareFactorizationMachinePredictionTransformer FieldAwareFactorizationMachineTrainer The IEstimator to predict a target using a field-aware factorization machine model trained using a stochastic gradient method. FieldAwareFactorizationMachineTrainer.Options HingeLoss Hinge Loss, commonly used in classification tasks. KMeansModelParameters KMeansTrainer The IEstimator for training a KMeans clusterer KMeansTrainer.Options Options for the KMeansTrainer as used in KMeansTrainer(Options). LbfgsLogisticRegressionBinaryTrainer The IEstimator to predict a target using a linear logistic regression model trained with L-BFGS method. LbfgsLogisticRegressionBinaryTrainer.Options LbfgsMaximumEntropyMulticlassTrainer The IEstimator to predict a target using a maximum entropy multiclass classifier trained with L-BFGS method. LbfgsMaximumEntropyMulticlassTrainer.Options LbfgsPoissonRegressionTrainer The IEstimator for training a Poisson regression model. LbfgsPoissonRegressionTrainer.Options Options for the LbfgsPoissonRegressionTrainer as used in LbfgsPoissonRegression(Options). LbfgsTrainerBase.OptionsBase Base options class for trainer estimators deriving fromLbfgsTrainerBase. LbfgsTrainerBase Base class for L-BFGS-based trainers. 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 to predict a target using a linear binary classification model trained with Linear SVM. LinearSvmTrainer.Options LinearTrainerBase LogLoss The Log Loss, also known as the Cross Entropy Loss. It is commonly used in classification tasks. MatrixFactorizationTrainer The IEstimator 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 ModelParametersBase Generic base class for all model parameters. ModelStatisticsBase Statistics for linear model parameters. NaiveBayesMulticlassModelParameters Model parameters for NaiveBayesMulticlassTrainer. NaiveBayesMulticlassTrainer The IEstimator for training a multiclass Naive Bayes model that supports binary feature values. OlsModelParameters Model parameters for OlsTrainer. OlsTrainer The IEstimator 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 for training a one-versus-all multi-class classifier that uses the specified binary classifier. OnlineGradientDescentTrainer The IEstimator 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 Base class for online linear trainers. Online trainers can be updated incrementally with additional data. PairwiseCouplingModelParameters Model parameters for PairwiseCouplingTrainer. PairwiseCouplingTrainer The IEstimator 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. PriorModelParameters Model parameters for PriorTrainer. PriorTrainer The IEstimator for predicting a target using a binary classification model. RandomizedPcaTrainer The IEstimator 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.BinaryOptionsBase Options for SdcaBinaryTrainerBase. SdcaBinaryTrainerBase 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. where TSubModel is LinearBinaryModelParameters and TCalibrator  is PlattCalibrator. SdcaLogisticRegressionBinaryTrainer The IEstimator 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 to predict a target using a maximum entropy multiclass classifier. The trained model MaximumEntropyModelParameters produces probabilities of classes. SdcaMaximumEntropyMulticlassTrainer.Options SdcaMulticlassTrainerBase.MulticlassOptions Options for the SdcaMulticlassTrainerBase. SdcaMulticlassTrainerBase The IEstimator 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 for training a binary logistic regression classification model using the stochastic dual coordinate ascent method. SdcaNonCalibratedBinaryTrainer.Options Options for the SdcaNonCalibratedBinaryTrainer. SdcaNonCalibratedMulticlassTrainer TheIEstimator to predict a target using a linear multiclass classifier. The trained model LinearMulticlassModelParameters produces probabilities of classes. SdcaNonCalibratedMulticlassTrainer.Options SdcaRegressionTrainer The IEstimator for training a regression model using the stochastic dual coordinate ascent method. SdcaRegressionTrainer.Options Options for the SdcaRegressionTrainer. SdcaTrainerBase.OptionsBase Options for the SDCA-based trainers. SdcaTrainerBase SgdBinaryTrainerBase.OptionsBase SgdBinaryTrainerBase SgdCalibratedTrainer The IEstimator 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 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 SymbolicSgdLogisticRegressionBinaryTrainer The IEstimator to predict a target using a linear binary classification model trained with the symbolic stochastic gradient descent. SymbolicSgdLogisticRegressionBinaryTrainer.Options TrainerEstimatorBase 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 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.

## Interfaces

 ICalculateFeatureContribution Allows support for feature contribution calculation by model parameters. IClassificationLoss ILossFunction 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 Interface for the Trainer Estimator.

## Enums

 KMeansTrainer.InitializationAlgorithm MatrixFactorizationTrainer.LossFunctionType Type of loss function.