【Machine Learning】【Andrew Ng】- Quiz(Week 6)

1. You train a learning algorithm, and find that it has unacceptably high error on the test set. You plot the learning curve, and obtain the figure below. Is the algorithm suffering from high bias, high variance, or neither?

   

   A. Neither

   B. High bias

   C. High variance

   答案：選B。high bias 和 high variance 表現在圖裡最大的差別就是gap。沒有gap的一定是high bias。

2. Suppose you have implemented regularized logistic regression to classify what object is in an image (i.e., to do object recognition). However, when you test your hypothesis on a new set of images, you find that it makes unacceptably large errors with its predictions on the new images. However, your hypothesis performs well (has low error) on the training set. Which of the following are promising steps to

   take? Check all that apply.

   A. Try using a smaller set of features.

   B. Try increasing the regularization parameter λ.

   C. Try decreasing the regularization parameter λ.

   D. Try evaluating the hypothesis on a cross validation set rather than the test set.

   答案：AB。訓練集表現很好是屬于high variance，為過拟合。是以可以改善模型的方法有A和B。特别注意，D選項錯誤，評價一個模型的性能隻能用測試集，CV集是用來調整模型degree的，也就是特征的次數。

3. Suppose you have implemented regularized logistic regression to predict what items customers will purchase on a web shopping site. However, when you test your hypothesis on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the hypothesis performs poorly on the training set. Which of the following might be promising steps to take? Check all that apply.

   A. Try evaluating the hypothesis on a cross validation set rather than the test set.

   B. Try decreasing the regularization parameter λ.

   C. Try adding polynomial features.

   D. Use fewer training examples.

   答案：BC。A選項不選原因同上。D，不管是哪種情況，為了提高模型性能，都需要更多的訓練樣本。

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

   A. Suppose you are training a regularized linear regression model. The recommended way to choose what value of regularization parameter λ to use is to choose the value of λ which gives the lowest test set error.

   B. Suppose you are training a regularized linear regression model.The recommended way to choose what value of regularization parameter λ to use is to choose the value of λ which gives the lowest training set error.

   C. The performance of a learning algorithm on the training set will typically be better than its performance on the test set.

   D. Suppose you are training a regularized linear regression model. The recommended way to choose what value of regularization parameter λ to use is to choose the value of λ which gives the lowest cross validation error.

   答案：CD。C，不管是哪種情況，訓練集的誤差一般都是比測試集的誤差大。D還不知道，歡迎評論補充，待更。

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

   A. If a learning algorithm is suffering from high variance, adding more training examples is likely to improve the test error.

   B. If the training and test errors are about the same, adding more features will not help improve the results.

   C. A model with more parameters is more prone to overtting and typically has higher variance.

   D. If a learning algorithm is suffering from high bias, only adding more training examples may not improve the test error significantly.

   答案：ACD。增加樣本數總是可以改善模型性能，是以A對B錯。

next step	type
Get more training examples	High Variance
Try smaller sets of features	High Variance
Try getting additional features	High Bias
Try adding polynomial	features High Bias
Try decreasing λ	High Bias
Try increasing λ	High Variance