NAME

       liblinear-train - train a linear classifier and produce a model

SYNOPSIS

       liblinear-train [options] training_set_file [model_file]

DESCRIPTION

       liblinear-train  trains  a  linear  classifier using liblinear and produces a model suitable for use with
       liblinear-predict(1).

       training_set_file is the file containing the data used for training.  model_file is the file to which the
       model will be saved. If model_file is not provided, it defaults to training_set_file.model.

       To obtain good performances, sometimes one needs to scale the data. This can be done with svm-scale(1).

OPTIONS

       A summary of options is included below.

       -s type
              Set the type of the solver (default 1):

               for multi-class classification

                 0 ... L2-regularized logistic regression (primal)

                 1 ... L2-regularized L2-loss support vector classification (dual)

                 2 ... L2-regularized L2-loss support vector classification (primal)

                 3 ... L2-regularized L1-loss support vector classification (dual)

                 4 ... multi-class support vector classification

                 5 ... L1-regularized L2-loss support vector classification

                 6 ... L1-regularized logistic regression

                 7 ... L2-regularized logistic regression (dual)

               for regression

                 11 ... L2-regularized L2-loss support vector regression (primal)

                 12 ... L2-regularized L2-loss support vector regression (dual)

                 13 ... L2-regularized L1-loss support vector regression (dual)

       -c cost
              Set the parameter C (default: 1)

       -p epsilon
              Set the epsilon in loss function of epsilon-SVR (default: 0.1)

       -e epsilon
              Set the tolerance of the termination criterion

              -s 0 and 2:

                 |f'(w)|_2 <= epsilon*min(pos,neg)/l*|f'(w0)|_2, where f is
                 the primal function and pos/neg are the number of positive/negative data
                 (default: 0.01)

              -s 11:

                 |f'(w)|_2 <= epsilon*|f'(w0)|_2 (default 0.0001)

              -s 1, 3, 4 and 7:

                 Dual maximal violation <= epsilon; similar to libsvm (default: 0.1)

              -s 5 and 6:

                 |f'(w)|_inf <= epsilon*min(pos,neg)/l*|f'(w0)|_inf, where f is the primal
                 function (default: 0.01)

              -s 12 and 13:

                 |f'(alpha)|_1 <= epsilon |f'(alpha0)|, where f is the dual function (default 0.1)

       -B bias
              If bias >= 0, then instance x becomes [x; bias]; if bias < 0, then
              no bias term is added (default: -1)

       -wi weight
              Weights adjust the parameter C of different classes (see README for details)

       -v n   n-fold cross validation mode

       -C     Find parameters (C for -s 0, 2 and C, p for -s 11)

       -q     Quiet mode (no outputs).

       Option -v randomly splits the data into n parts and calculates cross validation accuracy on them.

       Option -C conducts cross validation under different parameters and finds the best  one.  This  option  is
       supported  only  by  -s  0,  -s  2  (for  finding  C)  and -s 11 (for finding C, p). If the solver is not
       specified, -s 2 is used.

EXAMPLES

       Train a linear SVM using L2-loss function:

                  liblinear-train data_file

       Train a logistic regression model:

                  liblinear-train -s 0 data_file

       Do five-fold cross-validation using L2-loss SVM, using a smaller stopping tolerance 0.001 instead of  the
       default 0.1 for more accurate solutions:

                  liblinear-train -v 5 -e 0.001 data_file

       Conduct cross validation many times by L2-loss SVM and find the parameter C which achieves the best cross
       validation accuracy:

                  train -C datafile

       For  parameter  selection  by  -C,  users  can  specify other solvers (currently -s 0, -s 2 and -s 11 are
       supported) and different number of CV folds. Further, users can use the -c option to specify the smallest
       C value of the search range. This option is useful when users  want  to  rerun  the  parameter  selection
       procedure  from  a specified C under a different setting, such as a stricter stopping tolerance -e 0.0001
       in the above example. Similarly, for -s 11, users can use the -p option to specify the maximal p value of
       the search range.

                  train -C -s 0 -v 3 -c 0.5 -e 0.0001 datafile

       Train four classifiers:

                         positive    negative        Cp  Cn
                         class 1     class 2,3,4     20  10
                         class 2     class 1,3,4     50  10
                         class 3     class 1,2,4     20  10
                         class 4     class 1,2,3     10  10

                  liblinear-train -c 10 -w1 2 -w2 5 -w3 2 four_class_data_file

       If there are only two classes, we train ONE model. The C values for the two classes are 10 and 50:

                  liblinear-train -c 10 -w3 1 -w2 5 two_class_data_file

       Output probability estimates (for logistic regression only) using liblinear-predict(1):

                  liblinear-predict -b 1 test_file data_file.model output_file

SEE ALSO

       liblinear-predict(1), svm-predict(1), svm-train(1), svm-scale(1)

AUTHORS

       liblinear-train was written by the LIBLINEAR authors at National  Taiwan  university  for  the  LIBLINEAR
       Project.

       This  manual  page  was  written by Christian Kastner <ckk@debian.org> for the Debian project (and may be
       used by others).

                                                October 21, 2019                              LIBLINEAR-TRAIN(1)