Uses of Interface
org.tribuo.regression.sgd.RegressionObjective
Package
Description
Provides an implementation of factorization machines for regression using Stochastic Gradient Descent.
Provides an implementation of linear regression using Stochastic Gradient Descent.
Provides regression loss functions for Stochastic Gradient Descent.
-
Uses of RegressionObjective in org.tribuo.regression.sgd.fm
ModifierConstructorDescriptionFMRegressionTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed, int factorizedDimSize, double variance, boolean standardise) Constructs an SGD trainer for a factorization machine.FMRegressionTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed, int factorizedDimSize, double variance, boolean standardise) Constructs an SGD trainer for a factorization machine.FMRegressionTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed, int factorizedDimSize, double variance, boolean standardise) Constructs an SGD trainer for a factorization machine. -
Uses of RegressionObjective in org.tribuo.regression.sgd.linear
ModifierConstructorDescriptionLinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, int minibatchSize, long seed) Constructs an SGD trainer for a linear model.LinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, int loggingInterval, long seed) Constructs an SGD trainer for a linear model.LinearSGDTrainer
(RegressionObjective objective, StochasticGradientOptimiser optimiser, int epochs, long seed) Constructs an SGD trainer for a linear model. -
Uses of RegressionObjective in org.tribuo.regression.sgd.objectives
Modifier and TypeClassDescriptionclass
Absolute loss (i.e., l1).class
Huber loss, i.e., a mixture of l2 and l1 losses.class
Squared loss, i.e., l2.