Neither

High bias

High variance

Try using a smaller set of features.

Get more training examples.

Use fewer training examples.

Try adding polynomial features.

Try adding polynomial features.

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

Try decreasing the regularization parameter λ.

Use fewer training examples.

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.

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.

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

We always prefer models with high variance (over those with high bias) as they will able to better fit the training set.

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.

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