# Regression in Machine Learning

Regression in Machine Learning, probably the most popular type of machine learning models, estimates the interactions between variables. It is a statistical way to model the connection between a dependent (target) and independent (predictor) variables with one or more than one independent variable. It will help us to understand the value of the dependent variable is changing corresponding to an independent variable when other independent variables are held fixed. It predicts continuous/real values for example temperature, salary, age, cost, etc.

Why is Regression in Machine Learning is Important?

It is essential for any machine learning model that involves continuous numbers, including a vast array of real-life application:

• Automobile
• Weather forecast
• Finance
• Time series forecasting

Terminologies Related to the Regression in Machine Learning:

1. Dependent variable – The main factor in regression that we want to understand or predict is called the dependent variable, also known as Target variable
2. Independent variable – Factors affecting the dependent variable or the factors used to predict the value of the dependent variable is known as an independent variable or predictor.
3. Outliers – It is an observation containing either very high or low value as compared to other values. It should be avoided because it hampers the result.
4. Multicollinearity – In case the independent variables are highly correlated with each and every apart from other variables, then such a condition is known as Multicollinearity. It shouldn’t be in the dataset since it produces a problem while ranking the most impacting variable.
5. Underfitting and Overfitting- In case our algorithm works perfectly with the training dataset but not effective with the test dataset, then such a problem is called Overfitting. And in case our algorithm doesn’t perform well while with the training dataset, then such an issue is called underfitting.

Types of Regression in Machine Learning:

• Linear Regression
• Logistics Regression
• Polynomial Regression
• Support Vector Regression
• Decision Tree Regression
• Random Forest Regression
• Ridge Regression
• Lasso Regression
• ElasticNet

In this figure Y is the dependent variable, X is the independent variable.

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