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Mean squared error = %g (regression)
Accuracy = %g%% (%d/%d)
Y@Error: cannot transpose testing instance matrix
transposelabel (1st argument) should be a vector (# of column is 1).
Length of label vector does not match # of instances.
Usage: [predicted_label, accuracy, decision_values/prob_estimates] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')
       [predicted_label] = predict(testing_label_vector, testing_instance_matrix, model, 'liblinear_options','col')
liblinear_options:
-b probability_estimates: whether to output probability estimates, 0 or 1 (default 0); currently for logistic regression only
-q quiet mode (no outputs)
col: if 'col' is setted testing_instance_matrix is parsed in column format, otherwise is in row format
Returns:
  predicted_label: prediction output vector.
  accuracy: a vector with accuracy, mean squared error, squared correlation coefficient.
  prob_estimates: If selected, probability estimate vector.
Error: label vector and instance matrix must be double
model file should be a struct array
Testing_instance_matrix must be sparse; use sparse(Testing_instance_matrix) first
probability output is only supported for logistic regression
unknown option
Error: can't read model: %s
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