xenial (1) mlpack_logistic_regression.1.gz

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)