COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Low accuracy in unseen data in job applications
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Very long and inefficient lines of code
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Error in compiling code
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All of the above
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Detailed explanation-1: -If our model does much better on the training set than on the test set, then we’re likely overfitting. For example, it would be a big red flag if our model saw 99% accuracy on the training set but only 55% accuracy on the test set.
Detailed explanation-2: -Overfitting is a term used in statistics that refers to a modeling error that occurs when a function corresponds too closely to a particular set of data. As a result, overfitting may fail to fit additional data, and this may affect the accuracy of predicting future observations.
Detailed explanation-3: -Overfitting is not when your train accuracy is really high (or even 100%). It is when your train accuracy is high and your test accuracy is low. it is not abnormal that your train accuracy is higher than your test accuracy.
Detailed explanation-4: -Underfitting means that your model makes accurate, but initially incorrect predictions. In this case, train error is large and val/test error is large too. Overfitting means that your model makes not accurate predictions. In this case, train error is very small and val/test error is large.