NAME

       mlpack_logistic_regression - l2-regularized logistic regression and prediction

SYNOPSIS

        mlpack_logistic_regression [-h] [-v] [-d double] [-m string] [-l string] [-L double] [-n int] [-O string] [-o string] [-M string] [-s double] [-T string] [-e double] [-t string] -V

DESCRIPTION

       An  implementation of L2-regularized logistic regression using either the L-BFGS optimizer
       or SGD (stochastic gradient descent). This solves the regression problem

         y = (1 / 1 + e^-(X * b))

       where y takes values 0 or 1.

       This program allows loading a logistic regression model from a file  (-i)  or  training  a
       logistic  regression  model  given  training  data  (-t), or both those things at once. In
       addition, this program allows classification on a test dataset  (-T)  and  will  save  the
       classification results to the given output file (-o). The logistic regression model itself
       may be saved with a file specified using the -m option.

       The training data given with the -t option should have class labels as its last  dimension
       (so,  if  the  training  data  is  in  CSV  format,  labels  should  be  the last column).
       Alternately, the -l (--labels_file) option may be used  to  specify  a  separate  file  of
       labels.

       When  a  model  is  being  trained,  there are many options. L2 regularization (to prevent
       overfitting) can be specified with the -l option, and the  optimizer  used  to  train  the
       model  can  be  specified  with  the  --optimizer  option.   Available  options  are 'sgd'
       (stochastic gradient descent) and 'lbfgs' (the L-BFGS optimizer). There are  also  various
       parameters  for the optimizer; the --max_iterations parameter specifies the maximum number
       of allowed iterations, and the --tolerance (-e)  parameter  specifies  the  tolerance  for
       convergence.   For  the  SGD  optimizer,  the --step_size parameter controls the step size
       taken at each iteration by the optimizer. If the  objective  function  for  your  data  is
       oscillating  between  Inf  and  0,  the  step  size  is probably too large. There are more
       parameters for the SGD and L-BFGS optimizers, but the C++ interface must be used to access
       these.

       Optionally,  the  model  can  be  used to predict the responses for another matrix of data
       points, if --test_file is specified. The  --test_file  option  can  be  specified  without
       --input_file, so long as an existing logistic regression model is given with --model_file.
       The output predictions from the logistic regression model are stored  in  the  file  given
       with --output_predictions.

       This  implementation  of logistic regression does not support the general multi-class case
       but instead only the two-class case. Any responses must be either 0 or 1.

OPTIONS

       --decision_boundary (-d) [double]  Decision  boundary  for  prediction;  if  the  logistic
       function for a point is less than the boundary, the class is taken to be 0; otherwise, the
       class is 1. Default value 0.5.

       --help (-h)
              Default help info.

       --info [string]
              Get help on a specific module or option.   Default  value  ''.   --input_model_file
              (-m) [string] File containing existing model (parameters).  Default value ''.

       --labels_file (-l) [string]
              A  file  containing labels (0 or 1) for the points in the training set (y). Default
              value ''.

       --lambda (-L) [double]
              L2-regularization parameter for training.  Default value 0.

       --max_iterations (-n) [int]
              Maximum iterations for optimizer (0 indicates no limit). Default value 10000.

       --optimizer (-O) [string]
              Optimizer to use for training ('lbfgs' or ’sgd'). Default value 'lbfgs'.

       --output_file (-o) [string]
              If --test_file is specified, this file is where the  predicted  responses  will  be
              saved.  Default  value  ''.  --output_model_file (-M) [string] File to save trained
              logistic regression model to. Default value ''.

       --step_size (-s) [double]
              Step size for SGD optimizer. Default value 0.01.

       --test_file (-T) [string]
              File containing test dataset. Default value ’'.

       --tolerance (-e) [double]
              Convergence tolerance for optimizer. Default  value  1e-10.   --training_file  (-t)
              [string]  A file containing the training set (the matrix of predictors, X). Default
              value ''.

       --verbose (-v)
              Display informational messages and the full list of parameters and  timers  at  the
              end of execution.

       --version (-V)
              Display the version of mlpack.

ADDITIONAL INFORMATION

ADDITIONAL INFORMATION

       For  further  information,  including  relevant papers, citations, and theory, For further
       information, including relevant papers, citations, and theory, consult  the  documentation
       found  at  http://www.mlpack.org  or included with your consult the documentation found at
       http://www.mlpack.org or included with  your  DISTRIBUTION  OF  MLPACK.   DISTRIBUTION  OF
       MLPACK.

                                                                    mlpack_logistic_regression(1)