# What is Regularization?

## Understanding Regularization in Machine Learning

A machine learning model can quickly become overfitted or underfitted during training. To prevent this, we appropriately fit a model onto our test set using regularisation in machine learning. Regularization methods aid in obtaining the best model by lowering the likelihood of overfitting.

In this article we would be knowing about what is **regularization**, Types of **regularization**. Furthermore, we will discuss bias, variation, underfitting, and overfitting.

# Bias and Variance

## Bias

**Biases** are the underlying assumptions that data utilise to simplify the target function. Indeed, bias reduces the **model’s** **sensitivity** to lone data points and increases the generalizability of the data. It also reduces training time because the required function is simpler. **High** **bias** denotes a **higher** **reliability** assumption for the target function. The model may occasionally be **underfit** as a result.

## Variance

As a result of a model’s sensitivity to even the smallest differences in the dataset, **variance** is a type of **error** that occurs in **machine** **learning**. An algorithm would model the **noise** and **outliers** in the training set because of the large fluctuation. The phrase most frequently used to describe this is **overfitting**. The model in this instance learns every data point, therefore when tested on a fresh dataset, it cannot make correct predictions.

# Underfitting and Overfitting

## What is Underfitting?

A **model** is said to be **underfit** when it has not adequately learnt the patterns in the training data, which prevents it from correctly generalising to the new data. An **underfit** model performs **poorly** and produces poor **predictions** when applied to training data. Underfitting happens when the bias and variation are both low.

## What is Overfitting?

A **model** is considered to be overfit when it performs **exceptionally** **well** on **training** **data** but badly on **test** **data**. The noise and subtlety in the training data are picked up by the machine learning model in this situation, which has a detrimental impact on how well the model performs on test data. Overfitting might occur when low bias and high variability coexist.

# What is Regularization in Machine Learning?

The term “**regularization**” describes methods for **calibrating** machine **learning** **models** to reduce the adjusted loss function and avoid overfitting or underfitting.

We can properly fit our machine learning model on a particular test set using regularisation, which lowers the mistakes in the test set.

# Types of Regularization

The commonly used **regularization** techniques are :

- Lasso regularization (L1)
- Ridge regularization (L2)

## Lasso regularization (L1)

- we have a
**regularisation**method to lessen the model’s complexity is lasso regression. Least Absolute and Selection Operator are its acronyms. - With the exception of the penalty term’s absence of a square of weights, it is comparable to the Ridge Regression.

## Ridge regularization (L2)

- In order to improve our long-term forecasts,
**ridge****regression**is one of the forms of**linear****regression**that introduces a little level of bias. - Regularization methods like ridge regression are employed to make the model less complicated. It also goes by the name L2 regularisation.

**You can read my entire article on Lasso and Ridge Regularization.**

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# Conclusion

well I have good news for you I would be bringing some **more articles** to **explain machine learning concepts and models with codes** so leave a **comment** and tell me how excited are you about this.