Assume we are using regularized logistic regression for binary classification. Assume you have observed very large errors with its predictions on the new (unseen) data point, while the model has a low error on training data. Which of the following are steps would help to reduce the error on unseen data? Check all that apply. Use fewer training examples. Try adding polynomial features. O Try using a smaller set of features. Get more training examples.

Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
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Assume we are using regularized logistic regression for binary classification.
Assume you have observed very large errors with its predictions on the new
(unseen) data point, while the model has a low error on training data. Which of the
following are steps would help to reduce the error on unseen data? Check all that
apply.
Use fewer training examples.
Try adding polynomial features.
Try using a smaller set of features.
Get more training examples.
Transcribed Image Text:Assume we are using regularized logistic regression for binary classification. Assume you have observed very large errors with its predictions on the new (unseen) data point, while the model has a low error on training data. Which of the following are steps would help to reduce the error on unseen data? Check all that apply. Use fewer training examples. Try adding polynomial features. Try using a smaller set of features. Get more training examples.
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