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Suppose you have implemented a loss with the regularization term for a regression task to predict what items customers will purchase on a web shopping site. However, when you test your model on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the model performs poorly on the training set Which of the following might be promising steps to take? Check all that apply. Try increasing the number of layers of neural model. O Try evaluating your model on validation set rather than the test set. O Use fewer training examples. O Try decreasing the regularization parameter lambda

Suppose you have implemented a loss with the regularization term for a regression task to predict what items customers will purchase on a web shopping site. However, when you test your model on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the model performs poorly on the training set Which of the following might be promising steps to take? Check all that apply. Try increasing the number of layers of neural model. O Try evaluating your model on validation set rather than the test set. O Use fewer training examples. O Try decreasing the regularization parameter lambda

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Suppose you have implemented a loss with the regularization term for a regression task to predict what items customers will purchase on a web shopping site. However, when you test your model on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the model performs poorly on the training set. Which of the following might be promising steps to take? Check all that apply. O Try increasing the number of layers of neural model. Try evaluating your model on validation set rather than the test set. O Use fewer training examples. O Try decreasing the regularization parameter lambda
Transcribed Image Text:Suppose you have implemented a loss with the regularization term for a regression task to predict what items customers will purchase on a web shopping site. However, when you test your model on a new set of customers, you find that it makes unacceptably large errors in its predictions. Furthermore, the model performs poorly on the training set. Which of the following might be promising steps to take? Check all that apply. O Try increasing the number of layers of neural model. Try evaluating your model on validation set rather than the test set. O Use fewer training examples. O Try decreasing the regularization parameter lambda
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