This example uses "Housing" data set[1]from the UCI machine learning archive[2]described in http://archive.ics.uci.edu/ml/datasets/Housing. Download the data and save it in your current directory as a data file named housing.data.

The dataset has 506 observations. The first 13 columns contain the predictor values and the last column contains the response values. The goal is to predict the median value of owner-occupied homes in the Boston suburb area as a function of 13 predictors.

Load the data and define the response vector and predictor matrix.

load('housing.data');X =住房(:,1:13);y =住房(:,结束);

Fit a GPR model using subset of regressors ('sr') approximation method with Matern 3/2 ('Matern32') kernel function. Predict using the fully independent conditional ('fic') method.

gprMdl = fitrgp(X,y,'KernelFunction','Matern32',...'FitMethod','sr','PredictMethod','fic');

Compute the resubstitution predictions.

ypred = resubPredict(gprMdl);

Plot the predicted response values along with the actual response values.

plot(y,'r');% Plot original response valuesholdon; plot(ypred,'b--');% Plot predicted response valuesylabel('y');legend('Actual response','Predicted response','Location','SouthWest');axis([0 510 -10 55]); holdoff

Compute the resubstitution loss.

L = resubLoss(gprMdl)

L = 4.8478

Manually compute the regression loss.

n = length(y); L = (y-ypred)'*(y-ypred)/n

L = 4.8478

Load the sample data and store in atable.

loadfisheriristbl = table(meas(:,1),meas(:,2),meas(:,3),meas(:,4),species,...'VariableNames',{'meas1','meas2','meas3','meas4','species'});

Fit a GPR model using the first measurement as the response and the other variables as the predictors.

mdl = fitrgp(tbl,'meas1');

Predict the responses using the trained model.

ypred = predict(mdl,tbl);

Compute the mean absolute error.

n = height(tbl); y = tbl.meas1; fun = @(y,ypred,w) sum(abs(y-ypred))/n; L = resubLoss(mdl,'lossfun',fun)

L = 0.2345