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?

Neither

High variance

High bias

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.

Try adding polynomial features.

Use fewer training examples.

Get more training examples.

Try using a smaller set of features.

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.

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

Use fewer training examples.

Try decreasing the regularization parameter λ.

Try adding polynomial features.

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

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 λwhich gives the lowest cross validation error.

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 λwhich gives the lowest training set error.

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 λwhich gives the lowest test set error.

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

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

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

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.

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

When debugging learning algorithms, it is useful to plot a learning curve to understand if there is a high bias or high variance problem.