APPLICATION OF SUPERVISED LEARNING
SUPERVISED AND UNSUPERVISED LEARNING
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Adding a new feature to the model always results in equal or better performance on the training set?
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False
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True
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Either A or B
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None of the above
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Explanation:
Detailed explanation-1: -Adding many new features gives us more expressive models which are able to better fit our training set. If too many new features are added, this can lead to overfitting of the training set. Introducing regularization to the model always results in equal or better performance on examples not in the training set.
Detailed explanation-2: -Adding regularization may cause your classifier to incorrectly classify some training examples (which it had correctly classified when not using regularization, i.e. when 0=0). Using too large a value of can cause your hypothesis to overfit the data; this can be avoided by reducing .
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