7 Types of Regression in machine learning

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.