MATLAB: Nonlinear regression + Cross Validation = possible

cross-validationnonlinear regression

Hello. World. I want to know is it possible to perform cross validation on nonlinear regression model?

Best Answer

Cross-validation is used to assess the performance of classifiers.

Nonlinear regression does curve fitting (objective function parameter estimation).

These are two entirely different statistical techniques. What are you doing? How would you use cross-validation with your nonlinear regression?

Related Solutions

MATLAB: How do you obtain a vector of predicted classes generated after cross-valiation of a decision tree

Consider this:

load fisheriris
cp = cvpartition(species,'k',10);
F = @(xtr,ytr,xtest,ytest){ytest, eval(classregtree(xtr,ytr),xtest)};
result = crossval(F,meas,species,'partition',cp)

This uses the other syntax of crossval. The function F packages up the observed and fitted Y values for each fold of the 1-fold cross-validation. The results is a cell array with ten rows, one for each fold, with each row containing the corresponding observed and fitted values.

Alternatively you could write the cross-validation loop yourself. Loop over j and get test(cp,j) and training(cp,j) each time.

MATLAB: Confusion about the representation of Root Mean Square, R Squared …

Residuals and measures related to them are not a percentage. In the context of a one-dimensional situation, residuals are analogous to deviations from the mean, and measures derived from them are roughly analogous to the variance or standard deviation. (With heavy emphasis on ‘roughly’.)

The Coefficient of Determination (R-Squared) value could be thought of as a decimal fraction (though not a percentage), in a very loose sense. From the documentation:

Coefficient of determination (R-squared) indicates the proportionate amount of variation in the response variable y explained by the independent variables X in the linear regression model. The larger the R-squared is, the more variability is explained by the linear regression model.

So the higher the R-Squared value, the better the fit of the model to the data.

Best Answer

Related Solutions

MATLAB: How do you obtain a vector of predicted classes generated after cross-valiation of a decision tree

MATLAB: Confusion about the representation of Root Mean Square, R Squared …

Related Question