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


svm-predict [ -b probability_estimates ] [ -q ] test_data model_file [ output_file ]


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.


-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.

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