When doing least-squares regression with regularization (assuming that the optimization can be done exactly), increasing the value of the regularization parameter _ (a) will never decrease the training error. (b) will never increase the training error. (c) will never decrease the testing error. (d) will never increase the testing error. (e) may either increase or decrease the training error. (f) may either increase or decrease the testing error.
Answers
Answer: (b) will never increase the training error.
(f) may either increase or decrease the testing error.
Explanation:
Answer:
The correct answer of this question is Will never increase the training error .
Explanation:
Given - least-squares regression with regularization.
To Find - Write When doing least-squares regression with regularization (assuming that the optimization can be done exactly), increasing the value of the regularization parameter.
Will never increase the training error is least-squares regression with regularization (assuming that the optimization can be done exactly), increasing the value of the regularization parameter.
Regularized least squares (RLS) is a collection of algorithms for solving the least-squares problem with regularisation applied to further confine the solution. There are two basic reasons why RLS is employed. The first occurs when the linear system's number of variables exceeds the number of observations.
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