Microsoft.ML.Trainers Namespace

Classes

LinearTrainerBase<TTransformer,TModel>
MatrixFactorizationTrainer

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

MatrixFactorizationTrainer.Arguments
MultiClassClassificationTrainers

MultiClass Classification trainer estimators.

MultiClassNaiveBayesPredictor
MultiClassNaiveBayesTrainer
MultiClassNaiveBayesTrainer.Arguments
Ova
Ova.Arguments

Arguments passed to OVA.

OvaPredictor
Pkpd

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 Runtime.FastTree.FastTreeBinaryClassificationTrainer 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 simultaneously, rather than just one-by-one as would be needed for OVA.

Pkpd.Arguments

Arguments passed to PKPD.

PkpdPredictor
PoissonRegression

Trains a Poisson Regression model.

PoissonRegression.Arguments
PriorPredictor
PriorTrainer

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

PriorTrainer.Arguments
RandomPredictor

The predictor implements the Predict() interface. The predictor returns a uniform random probability and classification assignment.

RandomTrainer

A trainer that trains a predictor that returns random values

RandomTrainer.Arguments
Sdca

A component to train an SDCA model.

SdcaBinaryTrainer
SdcaBinaryTrainer.Arguments
SdcaMultiClassTrainer

Train an SDCA linear model.

SdcaMultiClassTrainer.Arguments
SdcaRegressionTrainer

Train an SDCA linear model.

SdcaRegressionTrainer.Arguments
SdcaTrainerBase<TArgs,TTransformer,TModel>.ArgumentsBase
SdcaTrainerBase<TArgs,TTransformer,TModel>.DualsTableBase

Encapsulates the common functionality of storing and retrieving the dual variables.

SdcaTrainerBase<TArgs,TTransformer,TModel>.IdToIdxLookup

A hash table data structure to store Id of type UInt128, and accommodates size larger than 2 billion. This class is an extension based on BCL. Two operations are supported: adding and retrieving an id with asymptotically constant complexity. The bucket size are prime numbers, starting from 3 and grows to the next prime larger than double the current size until it reaches the maximum possible size. When a table growth is triggered, the table growing operation initializes a new larger bucket and rehash the existing entries to the new bucket. Such operation has an expected complexity proportional to the size.

SdcaTrainerBase<TArgs,TTransformer,TModel>
StochasticGradientDescentClassificationTrainer
StochasticGradientDescentClassificationTrainer.Arguments

Enums

MatrixFactorizationTrainer.LossFunctionType
SdcaTrainerBase<TArgs,TTransformer,TModel>.MetricKind

Delegates

SdcaTrainerBase<TArgs,TTransformer,TModel>.Visitor