|LIBLINEAR-TRAIN(1)||General Commands Manual||LIBLINEAR-TRAIN(1)|
liblinear-train - train a linear classifier and produce a model
liblinear-train [options] training_set_file [model_file]
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).
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)
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
Find parameters (C for -s 0, 2 and C, p for -s 11)
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.
Train a linear SVM using L2-loss function:
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
liblinear-train was written by the LIBLINEAR authors at National Taiwan university for the LIBLINEAR Project.
This manual page was written by Christian Kastner <email@example.com> for the Debian project (and may be used by others).
|October 21, 2019|