# ▸ Regularization :

1. You are training a classification model with logistic regression. Which of the following statements are true? Check all that apply.
• 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 examples not in the training set.
• Adding a new feature to the model always results in equal or better performance on the training set.
• Adding many new features to the model helps prevent overfitting on the training set.

1. Suppose you ran logistic regression twice, once with , and once with . One of the times, you got parameters , and the other time you got . However, you forgot which value of corresponds to which value of . Which one do you think corresponds to ?
• When is set to 1, We use regularization to penalize large value of . Thus, the parameter, , obtained will in general have smaller values.

1. Suppose you ran logistic regression twice, once with , and once with . One of the times, you got parameters , and the other time you got . However, you forgot which value of corresponds to which value of . Which one do you think corresponds to ?
• When is set to 1, We use regularization to penalize large value of . Thus, the parameter, , obtained will in general have smaller values.

1. Which of the following statements about regularization are true? Check all that apply.
• Using a very large value of hurt the performance of your hypothesis; the only reason we do not set to be too large is to avoid numerical problems.
• Because logistic regression outputs values , its range of output values can only be “shrunk” slightly by regularization anyway, so regularization is generally not helpful for it.
• Consider a classification problem. Adding regularization may cause your classifier to incorrectly classify some training examples (which it had correctly classified when not using regularization, i.e. when λ = 0).
• Using too large a value of λ can cause your hypothesis to overfit the data; this can be avoided by reducing λ.

1. Which of the following statements about regularization are true? Check all that apply.
• Using a very large value of hurt the performance of your hypothesis; the only reason we do not set to be too large is to avoid numerical problems.
• Because logistic regression outputs values , its range of output values can only be “shrunk” slightly by regularization anyway, so regularization is generally not helpful for it.
• Because regularization causes J(θ) to no longer be convex, gradient descent may
not always converge to the global minimum (when λ > 0, and when using an
appropriate learning rate α).
• Using too large a value of λ can cause your hypothesis to underfit the data; this can be avoided by reducing λ.

1. In which one of the following figures do you think the hypothesis has overfit the training set?
• Figure: • Figure: • Figure: • Figure: ### Check-out our free tutorials on IOT (Internet of Things):

1. In which one of the following figures do you think the hypothesis has underfit the training set?
• Figure: • Figure: • Figure: • Figure: &
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