svm-predict - make predictions based on a trained SVM model file and test data

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svm-predict [ -bprobability_estimates ] [ -q ]test_datamodel_file[ output_file ]

svm-predictuses a Support Vector Machine specified by a given inputmodel_fileto make predictions for each of the samples intest_data

The format of this file is identical to the training_data file used insvm_train(1) and is just a sparse vector as follows:

<label> <index1>:<value1> <index2>:<value2> . . .

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.

.

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_fileis given, it will be used to specify the filename to store the predicted results, one per line, in the same order as thetest_datafile.

-b probability-estimates probability_estimatesis 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.

training_set_filemust 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.

No environment variables.

None documented; see Vapnik et al.

Please report bugs to the Debian BTS.

Chih-Chung Chang, Chih-Jen Lin <cjlin@csie.ntu.edu.tw>, Chen-Tse Tsai <ctse.tsai@gmail.com> (packaging)

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

Linux |
svm-predict (1) | MAY 2006 |