In regularized linear regression, we choose 0 to minimize 1 J(e) = 2m 'i=1 j=1 What if 2 is set to an extremely large value (perhaps too large for our problem, say 1 = 1010)? Algorithm results in underfitting (fails to fit even the training set). Gradient descent will fail to converge. Ob. Algorithm fails to eliminate overfitting. Od. Algorithm works fine: setting 1 to be very large can't hurt it.

In regularized linear regression, we choose 0 to minimize 1 J(e) = 2m 'i=1 j=1 What if 2 is set to an extremely large value (perhaps too large for our problem, say 1 = 1010)? Algorithm results in underfitting (fails to fit even the training set). Gradient descent will fail to converge. Ob. Algorithm fails to eliminate overfitting. Od. Algorithm works fine: setting 1 to be very large can't hurt it.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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