机器学习测试Week3_2Regularization

Week3_2Regularization

第 1 题

You are training a classification model with logistic
regression. Which of the following statements are true? Check
all that apply.

• Introducing regularization to the model always results in equal or better performance on the training set.
• Adding many new features to the model helps prevent overfitting ont the training set.
• Introducing regularization to the model always results in equal or better performance on examples not in the training set.
• Adding a new feature to the model always results in equal or better performance on the training set.
  *     答案: 4 *
  正则化方法的公式: J(θ)=12m[∑i=1m(hθ(x(i))−y(i))2+λ∑i=1nθ2j]
  * 选项1: 将正则化方法加入模型并不是每次都能取得好的效果,如果 λ 取得太大的化就会导致欠拟合. 这样不论对traing set 还是 examples都不好. 不正确 **
  * 选项2: more features能够更好的fit 训练集,同时也容易导致overfit,是more likely而不是prevent. 不正确 **
  * 选项3: 同1,将正则化方法加入模型并不是每次都能取得好的效果,如果 λ 取得太大的化就会导致欠拟合. 这样不论对traing set 还是 examples都不好. 不正确 **
  * 选项4: 新加的feature会提高train set的拟合度,而不是example拟合度. 正确 **

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第 2 题

Suppose you ran logistic regression twice, once with λ=0 , and once with λ=1 .
One of the times, you got parameters θ=[81.4712.69] , and the other time you got θ=[13.010.91] .
However, you forgot which value of λ corresponds to which value of θ. Which one do you think corresponds to λ=1?

• θ=[13.010.91]
• θ=[81.4712.69]
  *     答案: 1 *
  * 加入 λ 会让 θ 变小，当 λ 非常非常大时, θ1θ2...θn≈0 所以选择 θ=[13.010.91] *

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第 3 题

Which of the following statements about regularization are true? Check all that apply.

• Using too large a value of λ can cause your hypothesis to overfit the data; this can be avoided by reducing λ.
• Using a very large value of λ cannot hurt the performance of your hypothesis; the only reason we do not set λ to be too large is to avoid numerical problems.
• Consider a classification problem. Adding regularization may cause your classifier to incorrectly classify some training examples (which it had correctly classified when not using regularization, i.e. when λ=0).
• Because logistic regression outputs values 0≤hθ(x)≤1, its range of output values can only be “shrunk” slightly by regularization anyway, so regularization is generally not helpful for it.
  *     答案: 3 *
  正则化方法的公式: J(θ)=12m[∑i=1m(hθ(x(i))−y(i))2+λ∑i=1nθ2j]

* 选项1: λ 太大导致overfit不对,是underfit,当 λ 太大时 θ1θ2...θn≈0 .只有 θ0 起作用,拟合出来是一条直线. λ 太小才会导致overfit. 不正确 **
* 选项2: 同1. 不正确 **
* 选项3: 当 λ 没有选择好时,可能会导致训练效果还不如不加的 λ 好. 正确 **
* 选项4: “shrunk” slightly的是 θ , regularization是想要解决overfit. 不正确 **

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第 4 题

In which one of the following figures do you think the hypothesis has overfit the training set?

*     答案: 1 *

第 5 题

In which one of the following figures do you think the hypothesis has underfit the training set?

*     答案: 1 *