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fastLinear: fastLinear

  • Cikk

Creates a list containing the function name and arguments to train a Fast Linear model with rxEnsemble.

Usage

  fastLinear(lossFunction = NULL, l2Weight = NULL, l1Weight = NULL,
    trainThreads = NULL, convergenceTolerance = 0.1, maxIterations = NULL,
    shuffle = TRUE, checkFrequency = NULL, ...)

Arguments

lossFunction

Specifies the empirical loss function to optimize. For binary classification, the following choices are available:

  • logLoss: The log-loss. This is the default.
  • hingeLoss: The SVM hinge loss. Its parameter represents the margin size.
  • smoothHingeLoss: The smoothed hinge loss. Its parameter represents the smoothing constant.
    For linear regression, squared loss squaredLoss is currently supported. When this parameter is set to NULL, its default value depends on the type of learning:
  • logLoss for binary classification.
  • squaredLoss for linear regression.

l2Weight

Specifies the L2 regularization weight. The value must be either non-negative or NULL. If NULL is specified, the actual value is automatically computed based on data set. NULL is the default value.

l1Weight

Specifies the L1 regularization weight. The value must be either non-negative or NULL. If NULL is specified, the actual value is automatically computed based on data set. NULL is the default value.

trainThreads

Specifies how many concurrent threads can be used to run the algorithm. When this parameter is set to NULL, the number of threads used is determined based on the number of logical processors available to the process as well as the sparsity of data. Set it to 1 to run the algorithm in a single thread.

convergenceTolerance

Specifies the tolerance threshold used as a convergence criterion. It must be between 0 and 1. The default value is 0.1. The algorithm is considered to have converged if the relative duality gap, which is the ratio between the duality gap and the primal loss, falls below the specified convergence tolerance.

maxIterations

Specifies an upper bound on the number of training iterations. This parameter must be positive or NULL. If NULL is specified, the actual value is automatically computed based on data set. Each iteration requires a complete pass over the training data. Training terminates after the total number of iterations reaches the specified upper bound or when the loss function converges, whichever happens earlier.

shuffle

Specifies whether to shuffle the training data. Set TRUE to shuffle the data; FALSE not to shuffle. The default value is TRUE. SDCA is a stochastic optimization algorithm. If shuffling is turned on, the training data is shuffled on each iteration.

checkFrequency

The number of iterations after which the loss function is computed and checked to determine whether it has converged. The value specified must be a positive integer or NULL. If NULL, the actual value is automatically computed based on data set. Otherwise, for example, if checkFrequency = 5 is specified, then the loss function is computed and convergence is checked every 5 iterations. The computation of the loss function requires a separate complete pass over the training data.

...

Additional arguments.