吴恩达 Machine learning week3 quiz2 --Regularization答案解析

题1：
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 examples not in the training set.
• Introducing regularization to the model always results in equal or
  better performance on examples not in the training set.
• Adding many new features to the modelmakes it likely 同 overfit ont
  the training set. Introducing regularization to the model always
  results in equal or better performance on the training set.
• Adding a new feature to the model always results in equal or better
  performance on eamples not in the training set.
  解析：
  答案: 2
  -选项1:more features能够更好的fit 训练集，即新加的feature会提高training set的拟合度,而不是example拟合度。
  -选项2: more features能够更好的fit 训练集,同时也容易导致overfit,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 。
  
  解析：加入λ会让θ变小，当λ非常非常大时,θ1θ2…θn≈0。所以选择θ值较小的选项。

解析：
答案: 1
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 dunderfit the
  data.
• 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.
• Because regularization causes J(θ) to no longer be convex,gradient descent may not always convege to the globle minimum(when λ>0,and when using an appropriate learing rate a).
• 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.
• 选项1: λ太大导致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. 正确 *
• 选项2: 同1. 不正确
• 选项3: 不正确
• 选项4: “shrunk” slightly的是θ, regularization是想要解决overfit. 不正确