LIBLINEAR-TRAIN(1) General Commands Manual LIBLINEAR-TRAIN(1)
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 speci-
fied, -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 stop-
ping tolerance 0.001 instead of the default 0.1 for more accurate solu-
tions:
liblinear-train -v 5 -e 0.001 data_file
Conduct cross validation many times by L2-loss SVM and find the parame-
ter C which achieves the best cross validation accuracy:
train -C datafile
For parameter selection by -C, users can specify other solvers (cur-
rently -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 re-
run the parameter selection procedure from a specified C under a dif-
ferent 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 lib-
linear-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)
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