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Why identical outputs despite different inputs to a machine learning models

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RICHARD BIGEGA on 4 Mar 2019
Commented: RICHARD BIGEGA on 6 Mar 2019
why do I get the same predicted values despite having different inputs in a SVM model. For example, suppose the training data is matrixA, and the Two different Prediction data are MatrixC and MatrixD. Why is the predicted values identical?
MatrixB is a concatination of MatrixA with another Matrix---
I appreciate any help I can get?


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Answers (1)

Bernhard Suhm
Bernhard Suhm on 6 Mar 2019
Of course it could predict the same category for different kinds of inputs, especially if there aren't a lot of categories... Or for some reason, only the features represented by matrixA determined the final model, and the training of B ignored the additional features provided in the concatenated matrix. Or what am I missing?

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RICHARD BIGEGA on 6 Mar 2019
My exercise is not classification. I am working on forecasting future values given a set of know past values X(x). I ampuzzled because when I train a model I get the same outputs in some instances dispite that the sets on which I am doing predictions are different. Below is an example of the matrix whicth the outputs in redCapture.JPG

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