Classification vs Regression in Machine Learning
Last Updated : 04 Apr, 2025
Classification and regression are two primary tasks in supervised machine learning, where key difference lies in the nature of the output: classification deals with discrete outcomes (e.g., yes/no, categories), while regression handles continuous values (e.g., price, temperature).
Both approaches require labeled data for training but differ in their objectives—classification aims to find decision boundaries that separate classes, whereas regression focuses on finding the best-fitting line to predict numerical outcomes. Understanding these distinctions helps in selecting the right approach for specific machine learning tasks.
Classification vs Regression in Machine LearningFor example, it can determine whether an email is spam or not, classify images as "cat" or "dog," or predict weather conditions like "sunny," "rainy," or "cloudy." with decision boundary and regression models are used to predict house prices based on features like size and location, or forecast stock prices over time with straight fit line.
What is Regression in Machine Learning?
Regression algorithms predict a continuous value based on input data. This is used when you want to predict numbers such as income, height, weight, or even the probability of something happening (like the chance of rain). Some of the most common types of regression are:
- Simple Linear Regression: Models the relationship between one independent variable and a dependent variable using a straight line.
- Multiple Linear Regression: Predicts a dependent variable based on two or more independent variables.
- Polynomial Regression: Models nonlinear relationships by fitting a curve to the data.
What is Classification in Machine Learning?
Classification is used when you want to categorize data into different classes or groups. For example, classifying emails as "spam" or "not spam" or predicting whether a patient has a certain disease based on their symptoms. Here are some common types of classification models:
- Decision Tree Classification: Builds a tree where each node represents a test case for an attribute, and branches represent possible outcomes.
- Random Forest Classification: Uses an ensemble of decision trees to make predictions, improving accuracy by averaging the results from multiple trees.
- K-Nearest Neighbor (KNN): Classifies data points based on the 'k' nearest neighbors using feature similarity.
Decision Boundary vs Best-Fit Line
When teaching the difference between classification and regression in machine learning, a key concept to focus on is the decision boundary (used in classification) versus the best-fit line (used in regression). These are fundamental tools that help models make predictions, but they serve distinctly different purposes.
1. Decision Boundary in Classification
It is an surface or line that separates data points into different classes in a feature space. It can be linear (a straight line) or non-linear (a curve), depending on the complexity of the data and the algorithm used. For example:
- A linear decision boundary might separate two classes in a 2D space with a straight line (e.g., logistic regression).
- A more complex model, may create non-linear boundaries to better fit intricate datasets.
Decision Boundary in ClassificationDuring training classifier learns to partition the feature space by finding a boundary that minimizes classification errors.
- For binary classification, this boundary separates data points into two groups (e.g., spam vs. non-spam emails).
- In multi-class classification, multiple boundaries are created to separate more than two classes.
The decision boundary is not inherent to the training data but rather depends on the classifier used; we will understand more about classifiers in next chapter.
2. Best-Fit Line in Regression
In regression, a best-fit line (or regression line) represents the relationship between independent variables (inputs) and a dependent variable (output). It is used to predict continuous numerical values capturing trends and relationships within the data, allowing for accurate predictions of continuous variables. The best-fit line can be linear or non-linear:
- A straight line is used for linear regression.
- Curves are used for more complex regressions, like polynomial regression
Best-Fit Line in RegressionThe plot demonstrates Regression, where both Linear and Polynomial models are used to predict continuous target values based on the input feature, in contrast to Classification, which would create decision boundaries to separate discrete classes.
Classification Algorithms
There are different types of classification algorithms that have been developed over time to give the best results for classification tasks. Don’t worry if they seem overwhelming at first—we’ll dive deeper into each algorithm, one by one, in the upcoming chapters.
Regression Algorithms
There are different types of regression algorithms that have been developed over time to give the best results for regression tasks.
Comparison between Classification and Regression
Feature | Classification | Regression |
|---|
Output type | In this problem statement, the target variables are discrete. Discrete categories (e.g., "spam" or "not spam") | Continuous numerical value (e.g., price, temperature). |
Goal | To predict which category a data point belongs to. | To predict an exact numerical value based on input data. |
Example problems | Email spam detection, image recognition, customer sentiment analysis. | House price prediction, stock market forecasting, sales prediction. |
Evaluation metrics | Evaluation metrics like Precision, Recall, and F1-Score | Mean Squared Error, R2-Score, , MAPE and RMSE. |
Decision boundary | Clearly defined boundaries between different classes. | No distinct boundaries, focuses on finding the best fit line. |
Common algorithms | Logistic regression, Decision trees, Support Vector Machines (SVM) | Linear Regression, Polynomial Regression, Decision Trees (with regression objective). |
Classification vs Regression : Conclusion
Classification trees are employed when there's a need to categorize the dataset into distinct classes associated with the response variable. Often, these classes are binary, such as "Yes" or "No," and they are mutually exclusive. While there are instances where there may be more than two classes, a modified version of the classification tree algorithm is used in those scenarios.
On the other hand, regression trees are utilized when dealing with continuous response variables. For instance, if the response variable represents continuous values like the price of an object or the temperature for the day, a regression tree is the appropriate choice.
There are situations where a blend of regression and classification approaches is necessary. For instance, ordinal regression comes into play when dealing with ranked or ordered categories, while multi-label classification is suitable for cases where data points can be associated with multiple classes at the same time.