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##### Asked by: Maiol Lurje

technology and computing artificial intelligence# What is regularized linear regression?

Last Updated: 24th June, 2020

**Regularization**. This is a form of

**regression**, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible

**model**, so as to avoid the risk of overfitting. A simple relation for

**linear regression**looks like this.

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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.