我正在上stanford Machine Learning这门课，第三周的编程作业刚刚交过了，但是关于Regularization的这两道题试了两次都没过，我也重新看了视频，觉得没有过多说这两道题里面的内容，有明白的童鞋能否帮忙讲解一下，非常感谢帮忙。
You are training a classification model with logistic regression. Which of the following statements are true? Check all that apply.
A：Adding many new features to the model helps prevent overfitting on the training set.
B：Introducing regularization to the model always results in equal or better performance on the training set.
C：Adding a new feature to the model always results in equal or better performance on the training set.
D：Introducing regularization to the model always results in equal or better performance on examples not in the training set.
Which of the following statements about regularization are true? Check all that apply.
A：Because logistic regression outputs values 0≤hθ(x)≤1, it's range of output values can only be "shrunk" slightly by regularization anyway, so regularization is generally not helpful for it.
B：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.
C：Using too large a value of λ can cause your hypothesis to overfit the data; this can be avoided by reducing λ.
D：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).