text: Question 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?
  
  Learning curve with equal, high error on both training and test sets
  - Your Answer		Score	Explanation
  - High bias			
  - No High variance	Inorrect	0.00	This learning curve shows high error on both the training and test sets, so the algorithm is suffering from high bias.
  Neither	
  
  
  ### Question 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.
  Your Answer		Score	Explanation
  Try evaluating the hypothesis on a cross validation set rather than the test set.	Correct	0.25	A cross validation set is useful for choosing the optimal non-model parameters like the regularization parameter λ, but the train / test split is sufficient for debugging problems with the algorithm itself.
  Get more training examples.	Inorrect	0.00	The gap in errors between training and test suggests a high variance problem in which the algorithm has overfit the training set. Adding more training data will increase the complexity of the training set and help with the variance problem.
  Try using a smaller set of features.	Inorrect	0.00	The gap in errors between training and test suggests a high variance problem in which the algorithm has overfit the training set. Reducing the feature set will ameliorate the overfitting and help with the variance problem.
  Use fewer training examples.	Inorrect	0.00	The gap in errors between training and test suggests a high variance problem in which the algorithm has overfit the training set. Using fewer training examples will only exacerbate the overfitting.
  Total		0.25 / 1.00	
  
  ### Question 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.
  Your Answer		Score	Explanation
  - Try increasing the regularization parameter λ.	Correct	0.25	The poor performance on both the training and test sets suggests a high bias problem. Increasing regularization will decrease the fit of the hypothesis to the data, exacerbating the high bias problem.
  - Try evaluating the hypothesis on a cross validation set rather than the test set.	Correct	0.25	A cross validation set is useful for choosing the optimal non-model parameters like the regularization parameter λ, but the train / test split is sufficient for debugging problems with the algorithm itself.
  - Try adding polynomial features.	Inorrect	0.00	The poor performance on both the training and test sets suggests a high bias problem. Adding more complex features will increase the complexity of the hypothesis, thereby improving the fit to both the train and test data.
  - Try to obtain and use additional features.	Correct	0.25	The poor performance on both the training and test sets suggests a high bias problem. Using additional features will increase the complexity of the hypothesis, thereby improving the fit to both the train and test data.
  Total		0.75 / 1.00	
  
  ### Question 4
  Which of the following statements are true? Check all that apply.
  Your Answer		Score	Explanation
  It is okay to use data from the test set to choose the regularization parameter λ, but not the model parameters (θ).	Correct	0.25	You should not use test set data in choosing the regularization parameter, as it means the test error will not be a good estimate of generalization error.
  Suppose you are training a logistic regression classifier using polynomial features and want to select what degree polynomial (denoted d in the lecture videos) to use. After training the classifier on the entire training set, you decide to use a subset of the training examples as a validation set. This will work just as well as having a validation set that is separate (disjoint) from the training set.	Correct	0.25	This will not work as well as using a separate cross validation set, since the model parameters have already been fit to training data, so using training data for validation will not give an accurate estimate of test set error.
  The performance of a learning algorithm on the training set will typically be better than its performance on the test set.	Inorrect	0.00	The learning algorithm finds parameters to minimize training set error, so the performance should be better on the training set than the test set.
  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.	Correct	0.25	The cross validation lets us find the "just right" setting of the regularization parameter given the fixed model parameters learned from the training set.
  Total		0.75 / 1.00	
  
  ### Question 5
  Which of the following statements are true? Check all that apply.
  Your Answer		Score	Explanation
  - A model with more parameters is more prone to overfitting and typically has higher variance.	Inorrect	0.00	More model parameters increases the model's complexity, so it can more tightly fit data in training, increasing the chances of overfitting.
  - If a learning algorithm is suffering from high bias, only adding more training examples may not improve the test error significantly.	Correct	0.25	With high bias, the model is not fitting the training data currently present, so adding more data is unlikely to help.
  - If a neural network has much lower training error than test error, then adding more layers will help bring the test error down because we can fit the test set better.	Correct	0.25	With lower training than test error, the model has high variance. Adding more layers will increase model complexity, making the variance problem worse.
  - When debugging learning algorithms, it is useful to plot a learning curve to understand if there is a high bias or high variance problem.	Inorrect	0.00	The shape of a learning curve is a good indicator of bias or variance problems with your learning algorithm.
  Total		0.50 / 1.00	
  