3. Which of the following are reasons for using feature scaling?
It speeds up gradient descent by making it require fewer iterations to get to a good solution.
【解析】Feature scaling speeds up gradient descent by avoiding many extra iterations that are required when one or more features take on much larger values than the rest.
The cost function J(θ) for linear regression has no local optima.
The magnitude of the feature values are insignificant in terms of computational cost.
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