You have built a regression model to predict future crops of tomato based on a range of growing conditions. The model seems to fit well to the training data, but when any new data is tested it performs very poorly. How would you typically describe a model where this occurs? This is described as a well performing model. The model can't be expected to Press 1 get it right all the time. This is described as prediction error. The model happens to have got it wrong Press 2 for all of the test data. This is described as underfitting. The model hasn't picked out enough information to perform well yet. Press 3 This is described as overfitting. The model has picked out details in the data that don't generalize. Press 4

You have built a regression model to predict future crops of tomato based on a range of growing conditions. The model seems to fit well to the training data, but when any new data is tested it performs very poorly. How would you typically describe a model where this occurs? This is described as a well performing model. The model can't be expected to Press 1 get it right all the time. This is described as prediction error. The model happens to have got it wrong Press 2 for all of the test data. This is described as underfitting. The model hasn't picked out enough information to perform well yet. Press 3 This is described as overfitting. The model has picked out details in the data that don't generalize. Press 4

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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Question

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Multiple Choice
You have built a regression model to predict future crops of tomato based on a range
of growing conditions. The model seems to fit well to the training data, but when any
new data is tested it performs very poorly. How would you typically describe a model
where this occurs?
This is described as a well performing model. The model can't be expected to
Press 1
get it right all the time.
This is described as prediction error. The model happens to have got it wrong
Press 2
for all of the test data.
This is described as underfitting. The model hasn't picked out enough
information to perform well yet.
Press 3
This is described as overfitting. The model has picked out details in the data
that don't generalize.
Press 4

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