Good day, I wanted to check if my thoughts are on the right track about feature scaling. Suppose I have n features \mathbf{x} = \langle x_1,\ldots,x_n\rangle and m samples. Let \mu _j and \sigma_j denote the mean and standard deviation of feature x_j. The cost function J(\mathbf{w},b)_{\mathbf{x}} …

Interpreting the benefits of feature scaling

Course Q&A Machine Learning Specialization Supervised ML: Regression and Classification

Juan_Olano February 8, 2023, 11:58pm 15

Thanks for the reply @u5470152 !

So to summarize the item of the scope of normalization, we would normalize each feature independently, and at the end all would be in the 0-to-1 range, and done like this, each feature would preserve its distribution.

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Interpreting the benefits of feature scaling

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