Asked by: Maiol Lurjetechnology and computing artificial intelligence
What is regularized linear regression?
Last Updated: 24th June, 2020
Click to see full answer.
Also asked, what is lambda in linear regression?
When we have a high degree linear polynomial that is used to fit a set of points in a linear regression setup, to prevent overfitting, we use regularization, and we include a lambda parameter in the cost function. This lambda is then used to update the theta parameters in the gradient descent algorithm.
Also, what is the purpose of regularization? Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. The additional term controls the excessively fluctuating function such that the coefficients don't take extreme values.
Also to know, why do we need to regularize in regression?
The goal of regularization is to avoid overfitting, in other words we are trying to avoid models that fit extremely well to the training data (data used to build the model), but fit poorly to testing data (data used to test how good the model is). This is known as overfitting.
What does regularization mean?
In mathematics, statistics, and computer science, particularly in machine learning and inverse problems, regularization is the process of adding information in order to solve an ill-posed problem or to prevent overfitting. Regularization applies to objective functions in ill-posed optimization problems.