#
Microsoft.ML.Trainers
Namespace

The namespace containing the algorithms used for training a machine learning model.

## Classes

AveragedLinearOptions |
Arguments class for averaged linear trainers. |

AveragedLinearTrainer<TTransformer,TModel> |
Base class for averaged linear trainers. |

AveragedPerceptronTrainer |
The IEstimator<TTransformer> for the averaged perceptron trainer. |

AveragedPerceptronTrainer.Options |
Options for the AveragedPerceptronTrainer. |

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. If you need fast calculations, use the ComputeLogisticRegressionStandardDeviation implementation in the Microsoft.ML.Mkl.Components package, which makes use of hardware acceleration. Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients. |

ComputeLRTrainingStdThroughMkl | |

ExpLoss |
Exponential Loss |

FeatureContributionCalculator |
Support for feature contribution calculation. |

FieldAwareFactorizationMachineModelParameters | |

FieldAwareFactorizationMachinePredictionTransformer | |

FieldAwareFactorizationMachineTrainer |
Train a field-aware factorization machine for binary classification using ADAGRAD (an advanced stochastic gradient method). |

FieldAwareFactorizationMachineTrainer.Options | |

HingeLoss |
Hinge Loss |

KMeansModelParameters | |

KMeansTrainer |
K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. |

KMeansTrainer.Options | |

LbfgsLogisticRegressionBinaryTrainer |
Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function. |

LbfgsLogisticRegressionBinaryTrainer.Options | |

LbfgsMaximumEntropyMulticlassTrainer |
Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function. |

LbfgsMaximumEntropyMulticlassTrainer.Options | |

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

LbfgsPoissonRegressionTrainer.Options |
Options for the LbfgsPoissonRegressionTrainer. |

LbfgsTrainerBase<TOptions,TTransformer,TModel>.OptionsBase |
Base options for L-BFGS-based trainers. |

LbfgsTrainerBase<TOptions,TTransformer,TModel> |
Base class for L-BFGS-based trainers. |

LinearBinaryModelParameters |
The model parameters class for linear binary trainer estimators. |

LinearModelParameters | |

LinearModelParameterStatistics |
The statistics for linear predictor. |

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 |
The model parameters class for linear regression. |

LinearSvmTrainer |
Linear SVM that implements PEGASOS for training. See: http://ttic.uchicago.edu/~shai/papers/ShalevSiSr07.pdf |

LinearSvmTrainer.Options | |

LinearTrainerBase<TTransformer,TModel> | |

LogLoss | |

MatrixFactorizationTrainer |
Train a matrix factorization model. It factorizes the training matrix into the product of two low-rank matrices. |

MatrixFactorizationTrainer.Options |
Advanced options for the MatrixFactorizationTrainer. |

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> |
A base class for predictors producing |

ModelStatisticsBase |
The statistics for linear predictor. |

NaiveBayesMulticlassModelParameters | |

NaiveBayesMulticlassTrainer | |

OlsModelParameters |
A linear predictor for which per parameter significance statistics are available. |

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. |

OneVersusAllModelParameters |
Contains the model parameters and prediction functions for OneVersusAllTrainer. |

OneVersusAllTrainer | |

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. |

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 |
Contains the model parameters and prediction functions for the PairwiseCouplingTrainer. |

PairwiseCouplingTrainer |
In this strategy, a binary classification algorithm is trained on each pair of classes. The pairs are unordered but created with replacement: so, if there were three classes, 0, 1, 2, we would train classifiers for the pairs (0,0), (0,1), (0,2), (1,1), (1,2), and (2,2). For each binary classifier, an input data point is considered a positive example if it is in either of the two classes in the pair, and a negative example otherwise. At prediction time, the probabilities for each pair of classes is considered as the probability of being in either class of the pair given the data, and the final predictive probabilities out of that per class are calculated given the probability that an example is in any given pair. These two can allow you to exploit trainers that do not naturally have a
multiclass option, for example, using the FastTree Binary Classification
to solve a multiclass problem.
Alternately, it can allow ML.NET to solve a "simpler" problem even in the cases
where the trainer has a multiclass option, but using it directly is not
practical due to, usually, memory constraints. For example, while a multiclass
logistic regression is a more principled way to solve a multiclass problem, it
requires that the learner store a lot more intermediate state in the form of
L-BFGS history for all classes |

PcaModelParameters |
PCA is a dimensionality-reduction transform which computes the projection of the feature vector onto a low-rank subspace. |

PoissonLoss |
Poisson Loss. |

PoissonRegressionModelParameters |
The model parameters class for Poisson Regression. |

PriorModelParameters | |

PriorTrainer |
Learns the prior distribution for 0/1 class labels and outputs that. |

RandomizedPcaTrainer |
This trainer trains an approximate PCA using Randomized SVD algorithm Reference: https://web.stanford.edu/group/mmds/slides2010/Martinsson.pdf |

RandomizedPcaTrainer.Options | |

RegressionModelParameters | |

SdcaBinaryTrainerBase<TModelParameters>.BinaryOptionsBase |
Options base class for binary SDCA trainers. |

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 |

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. |

SdcaMaximumEntropyMulticlassTrainer |
The IEstimator<TTransformer> for training a maximum entropy classification model using the stochastic dual coordinate ascent method. The trained model MaximumEntropyModelParameters produces probabilities of classes. |

SdcaMaximumEntropyMulticlassTrainer.Options | |

SdcaMulticlassTrainerBase<TModel>.MulticlassOptions |
Options for the SdcaMulticlassTrainerBase<TModel>. |

SdcaMulticlassTrainerBase<TModel> |
The IEstimator<TTransformer> for training a multiclass linear classification model using the stochastic dual coordinate ascent method. |

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 |
The IEstimator<TTransformer> for training a multiclass linear model using the stochastic dual coordinate ascent method. The trained model LinearMulticlassModelParameters does not produces probabilities of classes, but we can still make decisions by choosing the class associated with the largest score. |

SdcaNonCalibratedMulticlassTrainer.Options | |

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. |

SgdNonCalibratedTrainer |
SgdNonCalibratedTrainer can train a linear classification model by minimizing any loss function which implements IClassificationLoss. |

SgdNonCalibratedTrainer.Options | |

SmoothedHingeLoss | |

SquaredLoss | |

StochasticTrainerBase<TTransformer,TModel> | |

SymbolicSgdLogisticRegressionBinaryTrainer |
Parallel Stochastic Gradient Descent trainer. |

SymbolicSgdLogisticRegressionBinaryTrainer.Options |
Advanced options for trainer. |

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. |

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<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. |

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