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Add the second parameter to return the prediction value

decision = svmob.predict(testData, true);
confidence = 1.0 / (1.0 + exp(-decision));

The distance itself is pretty ok, but if you want to have a confidence value in a range (0, 1), you can apply sigmoidal function to the result. One of such functions if logistic function.

Add the second parameter to return the prediction value

decision = svmob.predict(testData, true);
confidence = 1.0 / (1.0 + exp(-decision));

The distance itself is pretty ok, but if you want to have a confidence value in a range (0, 1), you can apply a sigmoidal function to the result. One of such functions if result, which could be a logistic function.

Add the second parameter to return the prediction value

decision = svmob.predict(testData, svm.predict(testData, true);
confidence = 1.0 / (1.0 + exp(-decision));

The distance itself is pretty ok, but if you want to have a confidence value in a range (0, 1), you can apply a sigmoidal function to the result, which could be a logistic function.

Add the second parameter to return the prediction value

decision = svm.predict(testData, true);
confidence = 1.0 / (1.0 + exp(-decision));

The distance itself is pretty ok, but if you want to have a confidence value in a range (0, 1), you can apply a sigmoidal function to the result, which could be a logistic function.