What is Linear Regression in Machine Learning?
A Deep Dive into Assumptions of Linear Regression.
Introduction
One of the most basic algorithms in statistics and machine learning is linear regression. It is widely used due to its simplicity, interpretability, and efficiency in modeling the relationship between a dependent variable and one or more independent variables. This comprehensive guide aims to provide a detailed understanding of what linear regression is, explain its key assumptions, explore various applications, and include practical coding examples to help you implement linear regression models.
What is Linear Regression in Machine Learning?
An approach for supervised learning called linear regression is used to forecast a continuous target variable using one or more input features. The primary goal of linear regression is to establish a linear relationship between the input variables (independent variables) and the target variable (dependent variable).
For linear regression to provide reliable predictions, certain assumptions need to be met. These assumptions ensure that the model is valid and the predictions are accurate. Basic principles of linear regression include:
- Linearity: The dependent variable…
