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Why fitlm function is giving wierd results?

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I am using following code
% PCA
[coeff, score, ~, ~, explained] = pca(X);
X_pca = score(:, 1:10);
% Split data
cv = cvpartition(size(X_pca, 1), 'HoldOut', 0.2);
idxTrain = training(cv);
idxTest = test(cv);
X_train = X_pca(idxTrain, :);
X_test = X_pca(idxTest, :);
Y_train = Y(idxTrain);
Y_test = Y(idxTest);
reg = fitlm(X_train, Y_train);
However, the rusults fitlm are coming wierd. Please suggest me how to get correct results.
Deva
  7 Comments
Devendra
Devendra on 14 Apr 2024
Thanks a lot for your kind guidance. Certainly it has helped me to understand the basics of prediction of data.🙏🙏 Devendra

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Accepted Answer

Manikanta Aditya
Manikanta Aditya on 13 Apr 2024
Hope you are doing great!
The error message you’re seeing is because the predict function is expecting an input with the same number of columns as the original data used to train the model. In your case, the model was trained with scoreTrain which has more than 3 columns, but you’re trying to predict with scoreTest which only has 3 columns (the principal components).
The issue arises from this line of code:
scoreTest = (X_test - mu)*coeff(:,1:idx)
Here, you’re reducing the dimensionality of your test set to 3 principal components, but your model was trained on the full set of principal components in scoreTrain.
To fix this, you should also limit the number of principal components in scoreTrain to 3. Here’s how you can do it:
scoreTrain = scoreTrain(:,1:idx);
reg = fitlm(scoreTrain, Y_train,'y ~ x1*x2*x3-x1:x2:x3');
Now, scoreTrain and scoreTest have the same number of columns, and you should be able to use the predict function without errors. Remember, the dimensions of the input for training and prediction must always match.
I hope this helps, let me know.
  4 Comments
Image Analyst
Image Analyst on 13 Apr 2024
Because you're deleting your posts, you will probably have difficulty finding people to want to help you anymore.
Devendra
Devendra on 13 Apr 2024
I am very sorry for my mistake. It will not happen again. Actually I deleted the post before realizing that already my friends have commented on it. My real intention was not to waste time of my friends on this issue since the problem is resolved. However it will not happen again in future. Devendra

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