And now we come to the final part—in order to compare models, we would like to cross-validate the model. We've already set aside a portion of the data. Now, we will have to test the model on the data that was set aside, and compute a score.
The score we'll be using is a Root Mean Square Error. It's used because it's simple and straightforward to understand:
// VERY simple cross validation var MSE float64 for i, row := range testingSet { pred, err := r.Predict(row) mHandleErr(err) correct := testingYs[i] eStar := correct - pred e2 := eStar * eStar MSE += e2 } MSE /= float64(len(testingSet)) fmt.Printf("RMSE: %v\n", math.Sqrt(MSE))
With this, now we're really ready to run the regression analysis.
