What Is Linear Regression In Machine Learning: Machine learning is transforming industries worldwide, and at its core lies one of the simplest yet most powerful algorithms—linear regression. Whether you’re predicting stock prices, house values, or sales trends, linear regression plays a crucial role in data-driven decision-making.
But what is linear regression in machine learning, and why is it so widely used? How does it work? If you’re curious about these questions, this guide will explain everything in detail.
Let’s find out!
What Is Linear Regression in Machine Learning?
Linear regression is a supervised learning algorithm used to predict a continuous output based on input variables. It establishes a relationship between independent variables (features) and a dependent variable (target) using a straight-line equation.
The core idea is simple: “The change in the independent variable(s) leads to a proportional change in the dependent variable.”
Mathematically, the equation of linear regression is:
ini
Y = mX + c
Where:
- Y = Predicted output
- X = Input feature
- m = Slope (weight of the feature)
- c = Intercept (constant)
This equation helps in predicting outcomes based on historical data, making it a fundamental technique in machine learning.
Why Use Linear Regression?
You might wonder, why use linear regression when there are so many machine learning models? Here’s why:
- Easy to Understand & Implement – Linear regression is one of the simplest machine learning models, making it ideal for beginners.
- Efficient for Small Datasets – When working with limited data, linear regression provides fast and effective results.
- Interpretable – Unlike complex algorithms, linear regression clearly shows the relationship between variables.
- Useful for Trend Analysis – Businesses use it for forecasting and decision-making, such as predicting sales based on past performance.
Fact: Linear regression was first introduced by Sir Francis Galton in the 19th century and remains one of the most used models in statistics and machine learning!
If you’re planning to enroll in a data science course at Ze Learning Labb, linear regression will likely be one of the first concepts you’ll learn.
How Linear Regression Works in Machine Learning?
Now that we know what it is, let’s understand how linear regression works in machine learning step by step:
Step 1: Data Collection & Preprocessing
- Gather historical data and clean it by removing missing or irrelevant values.
Step 2: Defining the Model
- The model tries to fit a straight-line equation that best represents the data.
Step 3: Training the Model
- The model finds the best values for m (slope) and c (intercept) using gradient descent and least squares method.
Step 4: Evaluating Performance
- The accuracy of the model is checked using metrics like R² score and Mean Squared Error (MSE).
Step 5: Making Predictions
- Once trained, the model predicts future values based on new inputs.
Example: If you train a model with past house prices and square footage, it can predict the price of a new house based on its size!
Types of Linear Regression in Machine Learning
Linear regression comes in different variations based on the number of independent variables. The types of linear regression in machine learning include:
1. Simple Linear Regression
- Involves only one independent variable.
- Example: Predicting house prices based on area size.
2. Multiple Linear Regression
- Involves two or more independent variables.
- Example: Predicting house prices based on area size, number of rooms, and location.
The key difference between linear regression and multiple regression is that multiple regression considers multiple factors, leading to more accurate predictions!
What Are the Assumptions of Linear Regression?
For linear regression to work effectively, certain conditions must be met. The assumptions of linear regression include:
- Linearity – The relationship between independent and dependent variables must be linear.
- No Multicollinearity – Independent variables should not be highly correlated.
- Homoscedasticity – The variance of residuals (errors) should remain constant.
- Normality of Residuals – The error terms should follow a normal distribution.
Ignoring these assumptions can lead to inaccurate predictions.
Cost Function of Linear Regression
To measure how well the model fits the data, we use a cost function of linear regression, which calculates the error between actual and predicted values.
The most commonly used cost function is:
r
J(m, c) = (1 / 2n) * Σ (Y_i – (mX_i + c))²
Where:
- J(m, c) = Cost function
- Y_i = Actual value
- X_i = Predicted value
- n = Number of observations
The model optimizes m and c by minimizing this cost function using techniques like Gradient Descent.
Advantages and Disadvantages of Linear Regression
Advantages:
- Simple and easy to interpret
- Computationally efficient
- Works well with small datasets
Disadvantages:
- Assumes a linear relationship, which may not always hold
- Sensitive to outliers
- Struggles with complex patterns
Despite some limitations, linear regression remains a foundational algorithm in machine learning.
Example of Linear Regression in Machine Learning
Let’s consider a real-world example of linear regression in machine learning:
Predicting sales based on advertising budget:
A company collects data on how much they spend on ads and the resulting sales. By applying linear regression, they can determine the relationship between ad spending and revenue.
Equation example:
ini
Sales = 5000 + 200 * Ad Budget
This means that for every ₹1,000 spent on ads, sales increase by ₹200,000!
On A Final Note…
Understanding what is linear regression in machine learning is crucial for anyone entering data science. It’s a simple yet powerful tool for predicting trends and making data-driven decisions.
If you’re eager to master machine learning, check out Ze Learning Labb’s advanced courses! They offer hands-on training, real-world projects, and expert guidance to boost your career.
Ready to dive deeper? Explore Ze Learning Labb’s courses today!