NAME¶
svm-predict - make predictions based on a trained SVM model file and test data
SYNOPSIS¶
svm-predict [ -b probability_estimates ] [ -q ] test_data
model_file [ output_file ]
DESCRIPTION¶
svm-predict uses a Support Vector Machine specified by a given input
model_file to make predictions for each of the samples in
test_data
The format of this file is identical to the training_data file used in
svm_train(1) and is just a sparse vector as follows:
- <label> <index1>:<value1>
<index2>:<value2> . . .
-
.
-
.
-
.
-
- There is one sample per line. Each sample consists of a
target value (label or regression target) followed by a sparse
representation of the input vector. All unmentioned coordinates are assumed
to be 0. For classification, <label> is an integer indicating the
class label (multi-class is supported). For regression, <label> is the
target value which can be any real number. For one-class SVM, it's not used
so can be any number. Except using precomputed kernels (explained in another
section), <index>:<value> gives a feature (attribute) value.
<index> is an integer starting from 1 and <value> is a real
number. Indices must be in an ASCENDING order. If you have label data
available for testing then you can enter these values in the test_data file.
If they are not available you can just enter 0 and will not know real
accuracy for the SVM directly, however you can still get the results of its
prediction for the data point.
-
If output_file is given, it will be used to specify the filename to
store the predicted results, one per line, in the same order as the
test_data file.
OPTIONS¶
- -b probability-estimates
- probability_estimates is a binary value indicating
whether to calculate probability estimates when training the SVC or SVR
model. Values are 0 or 1 and defaults to 0 for speed.
- -q
- quiet mode; suppress messages to stdout.
FILES¶
training_set_file must be prepared in the following simple sparse
training vector format:
- <label> <index1>:<value1>
<index2>:<value2> . . .
-
.
-
.
-
.
-
- There is one sample per line. Each sample consist of a
target value (label or regression target) followed by a sparse
representation of the input vector. All unmentioned coordinates are assumed
to be 0. For classification, <label> is an integer indicating the
class label (multi-class is supported). For regression, <label> is the
target value which can be any real number. For one-class SVM, it's not used
so can be any number. Except using precomputed kernels (explained in another
section), <index>:<value> gives a feature (attribute) value.
<index> is an integer starting from 1 and <value> is a real
number. Indices must be in an ASCENDING order.
-
ENVIRONMENT¶
No environment variables.
DIAGNOSTICS¶
None documented; see Vapnik et al.
BUGS¶
Please report bugs to the Debian BTS.
AUTHOR¶
Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai
<ctse.tsai@gmail.com> (packaging)
SEE ALSO¶
svm-train(1),
svm-scale(1)