How to predict each pixel of image using regression model?

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I have the following code that loops over each pixel of a .tif image to predict responses using ensemble of regression models.
X is a 753*6 numeric array which has 6 variables (also columns), and 753 rows. NR = 1380, NC = 1464.
I understand the error's meaning (The dimensions on both sides do not match each other), but I really do not know how to fix it. I imagine the result I need should be a 1380*1464 numeric array.
a = imread('LE71250521999276_b1.tif')
[NR,NC] = size(a);
Yfit = zeros(NR,NC);
for i = 1:NR
for j = 1:NC
Yfit(i,j) = predict(Mdl1999276,X);
end
end
ERROR: Assignment has more non-singleton rhs dimensions than non-singleton subscripts
Thank you for helping!!

Accepted Answer

Walter Roberson
Walter Roberson on 7 Jul 2017
Why are you using all of X to do the prediction each time?
Why are you reading in the image if you are not going to predict based on its values?
Ensembles often make one prediction per ensemble member per sample; if so then you might need to analyze a vector of results to decide what one output you want.
Predictions sometimes output a probability per class rather than a single class number.
  5 Comments
Noor Abbas
Noor Abbas on 19 Mar 2018
Does any paper or research that relevant with predict number of pixel using machine learning, Best Regards,
Philku Lee
Philku Lee on 28 Jun 2021
It might be related to
https://www.sciencedirect.com/science/article/pii/S0016236121003203

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