Libsvm dummy labels interferring with prediction

Question

I m trying to simulate out of sample prediction of a binary classifier using libsvm in matlab. My target variable (ie my label) is binary (-1 +1). Therefore, in my test set there are series for which i don t know the label. I created a new label for these observations (this label is 747). I found that in my predicted_label_test vector (see code below), this 747 label is included. So it means the prediction I get is influenced by the labels of the data included the test set, which is what I m supposed to predict? - The mistake may be in the way I use Libsvm read and write functions but i can t find it - many thanks!!

%%%%%%%%%% GET DATA FROM THE CSV FILE AND CONVERT THEM TO LIBSVM

addpath('C:\libsvm1\matlab'); %indicate position of the CSV file

ALLDATA = csvread('DATACSV.csv'); % read a csv file

labels = ALLDATA(:, 1); % labels are included in the first column of data

labels_sparse = sparse (labels); %? needed

features = ALLDATA(:, 4:end); % features start at 4th column

features_sparse = sparse(features); % features must be in a sparse matrix

libsvmwrite('TTT.train', labels_sparse, features_sparse); % write the file to libsvm format

[label_vector, predictors_matrix] = libsvmread('C:\libsvm1\matlab\TTT.train'); % read the file that was recorded in Libsvm format

%%%%% DEFINE VECTOR AND MATRIX SIZE

label_vector_train = label_vector (1:143,:);

predictors_matrix_train = predictors_matrix (1:143,:);

label_vector_test = label_vector (144:193,:);

predictors_matrix_test = predictors_matrix (144:193,:);

%PREDICTION

param = ['-q -c 2 -g 3'];

bestModel = svmtrain(label_vector_test, predictors_matrix_test, param);

[predicted_label_test, accuracy, prob_values] = svmpredict(label_vector_test, predictors_matrix_test, bestModel);

Answer 1

You are training a svm model with test data, when you should train it with training data:

bestModel = svmtrain(label_vector_test, predictors_matrix_test, param);

should be:

bestModel = svmtrain(label_vector_train, predictors_matrix_train, param);