Introduction to Regression in Machine Learning (For Beginners)

Machine learning (ML) is all about teaching computers to learn patterns from data. One of the most common and practical techniques in ML is regression—a method used to predict a continuous value. Whether you're estimating house prices, forecasting sales, or predicting temperatures, regression is a powerful tool to have in your machine learning toolkit.

In this article, we’ll break down what regression is, how it works, and how to get started using it—even if you’re just beginning.

What Is Regression?

Regression is a supervised learning technique. This means the model learns from a labeled dataset, where each example has both input features and a corresponding output value.

Think of it like this:

• Input: Size of a house, number of bedrooms, location
• Output: House price

The goal of regression is to find a mathematical relationship between inputs (also called features) and the output (also called target).

Types of Regression

There are several types of regression, but here are a few popular ones:

1. Linear Regression

• The simplest form.
• It models the relationship as a straight line.
• Example: Predicting salary based on years of experience.

2. Multiple Linear Regression

• Like linear regression, but with multiple input features.
• Example: Predicting house price using size, location, and age.

3. Polynomial Regression

• Fits a curve rather than a straight line.
• Useful when data shows a nonlinear relationship.

4. Logistic Regression

• Despite its name, it’s used for classification, not regression!
• Predicts probabilities (e.g., will a customer churn or not).

How Does Regression Work?

In linear regression, we assume the relationship between the inputs and output is linear, like this:

y = mx + b

Where:

• y is the output (prediction)
• x is the input feature
• m is the slope (how much y changes with x)
• b is the y-intercept

The learning part is finding the best values for m and b by minimizing the error between predicted and actual values. This is often done using a technique called least squares.

How to Use Regression (Step-by-Step)

Step 1: Prepare Your Data

• Collect and clean your dataset.
• Identify your input features (x) and output variable (y).
• Split the data into training and test sets.

Step 2: Choose a Model

from sklearn.linear_model import LinearRegression
model = LinearRegression()

Step 3: Train the Model

model.fit(X_train, y_train)

Step 4: Make Predictions

predictions = model.predict(X_test)

Step 5: Evaluate the Model

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• R² Score (how well the model explains the variability)

When to Use Regression

• Your target is a continuous number.
• You want to understand or forecast trends.
• You need interpretable models.

Tips for Beginners

• Visualize your data: Scatter plots can reveal patterns.
• Don’t forget feature scaling: Especially for polynomial regression.
• Avoid overfitting: Use validation and test sets properly.
• Start simple: Get good at linear regression before diving into advanced models like decision trees or neural networks.

Conclusion

Regression is a foundational concept in machine learning that opens the door to real-world predictions and insights. Once you’re comfortable with basic regression, you’ll find it much easier to understand more complex models down the road.