I am training a regression model to predict a label given a feature vector. Training and testing samples are drawn from 10 classes. I have trained SVM for regression as shown in the code below
void train::trainSVR(Mat data, vector labels)
{
Ptr trainData = TrainData::create(data, ml::ROW_SAMPLE, labels);
Ptr svr = SVM::create();
svr->setKernel(SVM::KernelTypes::POLY);
svr->setType(SVM::Types::NU_SVR);//For n-class classification problem with imperfect class separation
//svr->setC(5);
//svr->setP(0.01);
svr->setGamma(10.0);//for poly
svr->setDegree(0.1);//for poly
svr->setCoef0(0.0);//for poly
svr->setNu(0.1);
cout << "Training Support Vector Regressor..." << endl;
//svr->trainAuto(trainData);
svr->train(trainData);
bool trained = svr->isTrained();
if (trained)
{
cout << "SVR Trained. Saving..." << endl;
svr->save(".\\Trained Models\\SVR Model.xml");
cout << "SVR Model Saved." << endl;
}
}
I have predicted my model as shown in the function below
void train::predictSVR(Mat data, vector labels){
Mat_ output;
vector predicted;
//Create svm smart pointer and load trained model
Ptr svr = Algorithm::load(".\\Trained Models\\SVR Model.xml");
ofstream prediction;
prediction.open("SVR Prediction.csv", std::fstream::app);//open file for writing predictions in append mode
for (int i = 0; i < labels.size(); i++)
{
float pred = svr->predict(data.row(i));
prediction << labels[i] << "," << pred << endl;
}
}
The prediction gives me some value against every label. My questions are:-
1. What does the value against the label signify?
2. How do I retrieve Mean squared error (mse), support vectors and constants for the regression function `y=wx +b`. How do I get `b`, and `w`
Kindly advice.
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