Stanford Machine Learning Week 3: Regularization

5. You are training a classification model with logistic regression. Which of the following statements are true? Check all that apply.【D】 A. Introducing regularization to the model always results in equal or better performance on the training set.Introducing regularization to the model always results in equal or better performance on the training set.  【解析】If we introduce too much regularization, we can underfit the training set and have worse performance on the training set. B.Adding many new features to the model helps prevent overfitting on the training set. 【解析】Adding many new features gives us more expressive models which are able to better fit our training set. If too many new features are added, this can lead to overfitting of the training set. C. Adding a new feature to the model always results in equal or better performance on examples not in the training set. 【解析】Adding more features might result in a model that overfits the training set, and thus can lead to worse performs for examples which are not in the training set. D.Adding a new feature to the model always results in equal or better performance on the training set. 【解析】By adding a new feature, our model must be more (or just as) expressive, thus allowing it learn more complex hypotheses to fit the training set.

第一题我开始选了CD。我觉得D里说的对training set以外的数据预测的更好正是引入regularization的作用，提交时候被判错。
后来又想了想，如果本来就已经fit，或者regularization的不合适（比如λ过大），也不能保证起反作用。是不是应该这么理解？

fyswords 发表于 2017-8-7 17:12
第一题我开始选了CD。我觉得D里说的对training set以外的数据预测的更好正是引入regularization的作用，提 ...

你是对的。过大的lamda会导致underfit。