How do I create a cross validated linear regression model with fitlm ?
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I would like to know how I can perform cross validation on a fitlm model. All other functions for regression models support KFold as a function object. How does KFold work with fitlm as there is noch function object for cross validation implemented. I also tried crossval on a trained fitlm model which didn't work either. I hope someone can help me with my problem.
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Shubham Srivastava
el 14 de Feb. de 2017
You can perform a K-fold cross validation for the 'fitlm' function into K folds using the 'crossval' function. In order to do so, define a predictor function handle which uses 'fitlm' and then pass the predictor function handle to the 'crossval' function.
Mentioned below is a sample code snippet to do so:
% prediction function given training and testing instances
>> fcn = @(Xtr, Ytr, Xte) predict( fitlm(Xtr,Ytr), Xte);
% perform cross-validation, and return average MSE across folds
>> mse = crossval('mse', X, Y,'Predfun',fcn, 'kfold',10);
% compute root mean squared error
>> avrg_rmse = sqrt(mse)
Regards,
Shubham
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Mattias Blomfors
el 6 de Sept. de 2017
I share the same question as Mara. How do I use the cross validated trained model to make predictions? Also, how to get the parameters for the linear model?
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