Predict response after Lasso

I am using Lasso to remove redundant predictors by using cross-validated fits, essentially following this guide. To make predictions on the Train set, I multiplied the Train and B(:,FitInfo.IndexMinMSE) matrices. The predicted values somehow are not close to the actual response of the Train set. Am I missing something here? How do I predict response using coefficients obtained from Lasso?

 X = x2fx(X)
 rng default % For reproducibility
 [B,FitInfo] = lasso(X,Y,'CV',10);
 lassoPlot(B,FitInfo,'PlotType','CV'); % Look at MSE vs Lambda
 coeff = B(:,FitInfo.IndexMinMSE); % Regression coefficients for min MSE, use these coefficients.
 Ypredict = X * coeff; % Predicted response

Answer 1

[Lonoko]

The first column of X corresponds to the constant (see help for x2fx), but Matlab doesn't save the constant value in B (coef(1)=0), so your predictions are biased. The predicted response is: Ypredict = X * coeff + FitInfo.Intercept(IndexMinMSE);