Beginner’s Guide to Simple Linear Regression in Machine Learning: An Overview
Machine learning is a rapidly evolving field that has been generating a lot of buzz lately. Among a long list of machine learning models, simple linear regression is one of the most basic yet powerful. As a beginner in machine learning, you may have heard about simple linear regression but may not be familiar with what it is, how it works or why it’s important. In this article, we will provide you with a comprehensive guide on simple linear regression in machine learning.
What is Simple Linear Regression?
Simple linear regression is a statistical method that models the relationship between a dependent variable (Y) and an independent variable (X). In other words, it helps us to understand how a change in one variable will affect another.
For instance, let’s say we are interested in examining a relationship between the miles per gallon of a car and the engine’s horsepower. The independent variable is horsepower, and the dependent variable is the miles per gallon. The simple linear regression model will show us how a change in the horsepower affects the mileage of a car.
Assumptions of Simple Linear Regression
Before we dive into how simple linear regression works, it is essential to understand the underlying assumptions.
- Linearity: The relationship between the independent and dependent variable is linear.
- Independence: The observations within the data set are independent of each other.
- Homoscedasticity: The variance of residuals is constant across all levels of the independent variable.
- Normality: Residuals follow a normal distribution.
It’s worth noting that if the assumptions of simple linear regression are not met, it may lead to biased or inaccurate results.
How Simple Linear Regression Works?
Simple linear regression works by fitting a straight line that best represents the relationship between the independent and dependent variables. In machine learning terms, we use a training data set to estimate the coefficients of the linear equation and then use the test data set to evaluate the model’s performance.
In our previous car example, the simple linear regression equation would look like this:
Miles per gallon = β0 + β1 (horsepower)
β0 is the intercept and β1 is the slope of the line. The slope tells us how much the dependent variable (miles per gallon) changes with a one-unit increase in the independent variable (horsepower), while the intercept represents the expected value of the dependent variable when the independent variable is zero.
Applications of Simple Linear Regression
Simple linear regression has a wide range of applications across different domains. Some of the popular applications include:
- Marketing: Understanding the relationship between advertising spending and sales revenue.
- Finance: Predicting stock prices based on financial ratios.
- Healthcare: Modelling the relationship between a patient’s age and their blood pressure.
- Sports: Predicting the performance of an athlete based on their training regimen.
Conclusion
In conclusion, simple linear regression is a powerful tool that helps us predict the outcome of a dependent variable based on a change in the independent variable. Simple linear regression is just the beginning of the machine learning journey, and understanding how it works is crucial to developing more complex models. By following the assumptions of simple linear regression and carefully choosing your data, you can generate valuable insights that can help you make informed decisions.
