When I said normalize, I meant place the data into some normal form, like having the same "scale"
Here is an example to help
Suppose you have three points:
x=(1,2),(1,2),(3,2)
y=+1,1,+1
One way to normalize the data is to have the average squared value of each coordinate equal to 1. You would divide the first xcoordinate by
and the second coordinate by
. Now both coordinates are "normalized" so that the average squared value is 1.
Suppose instead you wanted to use the third point as a test point. Now you normalize the first 2 points. In this case you dont change the first coordinate and divide the second coordinate by 2, to get the normalized data. You learn on this normalized training data of 2 points and test the learned hypothesis on the 3rd point. Before you test the learned hypothesis, you need to rescale the test point with the same rescaling parameters that you used to normalize the 2 training data points.
Quote:
Originally Posted by curiosity
Thanks for the quick reply magdon. However, I didn't get this. What is the difference between normalizing and rescaling in this case? For the general case can you also please describe whether I need to also normalize my test data when evaluating the final model? Or in other words how should I evaluate the final model if I have used normalization during training? An example would be very appreciated...
