APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Some of the coefficient will become absolute zero
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Some of the coefficient will approach zero but not absolute zero
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Both A and B depending on the situation
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None of these
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Detailed explanation-1: -If your lambda value is too high, your model will be simple, but you run the risk of underfitting your data. Your model won’t learn enough about the training data to make useful predictions. If your lambda value is too low, your model will be more complex, and you run the risk of overfitting your data.
Detailed explanation-2: -It is said that because the shape of the constraint in LASSO is a diamond, the least squares solution obtained might touch the corner of the diamond such that it leads to a shrinkage of some variable. However, in ridge regression, because it is a circle, it will often not touch the axis.
Detailed explanation-3: -Ridge regression shrinks all regression coefficients towards zero; the lasso tends to give a set of zero regression coefficients and leads to a sparse solution. Note that for both ridge regression and the lasso the regression coefficients can move from positive to negative values as they are shrunk toward zero.