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Should I normalize data for regression?
When we do further analysis, like multivariate linear regression, for example, the attributed income will intrinsically influence the result more due to its larger value. But this doesn’t necessarily mean it is more important as a predictor. So we normalize the data to bring all the variables to the same range.
Why we should normalize data for neural network?
Among the best practices for training a Neural Network is to normalize your data to obtain a mean close to 0. Normalizing the data generally speeds up learning and leads to faster convergence.
Why should I normalize also the output data?
I’m new to data science and Neural Networks in general. Looking around many people say it is better to normalize the data between doing anything with the NN. I understand how normalizing the input data can be useful. However I really don’t see how normalizing the output data can help.
Why should I standardize the target variables ( column vectors )?
Search for Should I standardize the target variables (column vectors)? in that page. Standardizing target variables is typically more a convenience for getting good initial weights than a necessity.
Is it necessary to scale the target value in machine learning?
Normalizing the output will not affect shape of f, so it’s generally not necessary. The only situation I can imagine scaling the outputs has an impact, is if your response variable is very large and/or you’re using f32 variables (which is common with GPU linear algebra).
Why is it necessary to scale the target?
The functional form is the same but the learning rate for the second coordinate has to be adjusted to 1/1000 of that for the first coordinate to match it. If not coordinate two will dominate and the Δ vector will point more towards that direction. As a result it biases the delta to point across that direction only and makes the converge slower.