Mean squared error (why mean?)

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The discussion centers on the equation for mean squared error (MSE) in machine learning, specifically questioning the inclusion of the factor of 1/2 in the formula. The contributor notes that if it is a mean, the equation should include 1/N instead of 1/2. There is confusion regarding the variables y and t, as there is no indication that they are limited to two values. The contributor requests clarification on the source of the equation, emphasizing the need for proper references. The conversation highlights a common point of confusion in understanding MSE calculations in machine learning contexts.
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Hi
I found this equation in a machine learning book:
"we want to minimize the mean squared error:"
$E= \frac{1}{2} \sum_{n=1}^N (y-t)^2$

what I do not understand is the \frac{1}{2} , if it is a mean it should be \frac{1}{N},
why are they restricting to 2? In the text there is no reference to y or t being only 2. So it cannot be that N=2.
 
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If you ask a question here, please always be sure to include the necessary reference. What book did you read this in? Title, author, page number?
 
sunone said:
Hi
I found this equation in a machine learning book:
"we want to minimize the mean squared error:"
$E= \frac{1}{2} \sum_{n=1}^N (y-t)^2$

what I do not understand is the \frac{1}{2} , if it is a mean it should be \frac{1}{N},
why are they restricting to 2? In the text there is no reference to y or t being only 2. So it cannot be that N=2.

Fix Latex.
 
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