**Today let us see 7 types of regression** in machine learning which we can use in our data science solutions.

Regression in machine learning |

## 1. Lasso Regression

Lasso regression is a type of linear regression that uses shrinkage. Data values are shrunk towards a central point, like that of the mean. This is the most common technique to do regularization.## 2. Elastic Net Regularization

Elastic net is a regularization regression method that linearly combines the L1 and L2 penalties of the lasso and ridge regression.## 3. Kernel Ridge Regression

It solves a regression model where the loss function is the linear least squares function and regularization is given by the I2-norm.## 4. Gradient Boosting regression

It is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision tress.## 5. XGBoost

XGBoost is an algorithm that has recently been dominating applied machine learning and Kaggle competition for structured or tabular data.## 6. RobustScaler()

It scales features using statistics that area robust to outliers.## 7. LightGBM

LightGBM is a gradient boosting framework that uses tree based learning algorithms. It uses leaves of trees unlike others so loss is minimum.**These are the 7 types of regression in machine learning.**

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