ML 错题集
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week 2.
You'd like to use polynomial regression to predict a student's final exam score from their midterm exam score. Concretely, suppose you want to fit a model of the form
What is the normalized feature
【解析】mean normalization
Replace xi with xi-μi to make fetures have approximately zero mean.Do not apply to x0=1;
均值归一化
$$ x = \dfrac{x_i -avg }{max-min}$$
avg = (7921+5184+8836+4761)/4=6675.5
answer = (5184-(6675.5))/(8836-4761)
2.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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