Regression vs Classification in Machine Learning Explained!

This guide explains the differences between regression and classification in machine learning, highlighting their importance for data scientists and technologists. These methodologies are used for predictive modeling and solving specific problems. It provides a detailed examination of their characteristics, applications, advantages, and challenges, aiming to equip professionals with the knowledge to effectively use these tools in their data science endeavors.

In this article, you will learn about the difference between regression and classification in machine learning. We’ll explore classification vs regression, and clarify the distinctions between these two fundamental concepts.

What is Regression?

Regression algorithms predict continuous value from the provided input. A supervised learning algorithm uses real values to predict quantitative data like income, height, weight, scores or probability. Machine learning engineers and data scientists mostly use regression algorithms to operate distinct labeled datasets while mapping estimations.

Key Concepts in Regression

• Supervised Learning: Regression, a type of supervised learning, involves training the model on labeled data where the target variable is known. This allows the model to learn the relationship between the input features (independent variables) and the target variable (dependent variable).
• Continuous Target Variable: Unlike classification, which predicts discrete labels or classes, regression predicts a continuous numeric value. For example, predicting house prices, stock prices, temperature, or sales revenue are all regression problems where the target variable is a continuous value.

What is Classification? 

Classification is a procedure where a model or function separates data into discrete values, i.e., multiple classes of datasets using independent features. A form If-Then rule derives the mapping function. The values classify or forecast the different values like spam or not spam, yes or no, and true or false. An example of the discrete label includes predicting the possibility of an actor visiting the mall for a promotion, depending on the history of the events. The labels will be Yes or No.

Key Concepts in Classification

• Supervised Learning: Classification is a type of supervised learning where the model is trained on a labeled dataset. This means the dataset used for training contains both input features (independent variables) and the corresponding target labels (dependent variables).
• Categorical Target Variable: The target variable in classification is categorical, meaning it consists of class labels that represent different categories or classes.

Types of Regression

Let us now explore types of regression.

1. Linear Regression

Most preferable and simple to use, it applies linear equations to the datasets. Using a straight line, the relationship between two quantitative variables i.e., one independent and another dependent, is modeled in simple linear regression. A dependent variable’s multiple linear regression values can use more than two independent variables. It is applicable to predict marketing analytics, sales, and demand forecasting.

Equation: 𝑦=𝛽0+𝛽1𝑋1+𝛽2𝑋2+…+𝛽𝑛𝑋𝑛+𝜖y=β0​+β1​X1​+β2​X2​+…+βn​Xn​+ϵ

2. Polynomial Regression

To find or model the non-linear relationship between an independent and a dependent variable is called polynomial regression. It is specifically used for curvy trend datasets. Various fields like social science, economics, biology, engineering and physics use a polynomial function to predict the model’s accuracy and complexity. In ML, polynomial regression is applicable to predict customers’ lifetime values, stock and house prices. 

Equation: 𝑦=𝛽0+𝛽1𝑋+𝛽2𝑋2+…+𝛽𝑛𝑋𝑛+𝜖y=β0​+β1​X+β2​X2+…+βn​Xn+ϵ

3. Logistic Regression

Commonly known as the logit model, Logistic Regression understands the probable chances of the occurrence of an event. It uses a dataset comprising independent variables and finds application in predictive analytics and classification. 

Types of Classification

Let us now explore types of classification.

1. Binary Classification

When an input provides a dataset of distinct features describing each point, the output of the model delivered will be binary labeled representing the two classes i.e., categorical. For example, Yes or No, Positive or Negative.

Examples: Spam detection (spam or not spam), disease diagnosis (diseased or not diseased).

2. Multi-class Classification

In machine learning, multi-class classification provides more than two outcomes of the model. Their subtypes are one vs all/rest and multi-class classification algorithms. Multiclass does not rely on binary models and classifies the datasets into multiple classes. At the same time, OAA/OAR represents the highest probability and score from separate binary models trained for each class.

Examples: Handwritten digit recognition (0-9 digits), email categorization (spam, primary, social, promotions).

3. Decision Trees

Decisions and their consequences are in a tree-based model, where nodes of the decision tree confirm each node and edges show the consequence of that particular decision.

Also Read: Effective Strategies for Handling Missing Values in Data Analysis

How Regression Works

• Model Training: The regression model is trained using a dataset that includes both input features and their corresponding correct output values.
• Objective: The objective is to learn a function that best maps input variables (independent variables) to the output variable (dependent variable) in order to make accurate predictions on new, unseen data.
• Evaluation: Once trained, the model’s performance is evaluated using a variety of metrics depending on the specific regression task. Common evaluation metrics include Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R2 score), Mean Absolute Error (MAE), etc.

How Classification Works

• Model Training: The classification model is trained on a labeled dataset that includes both input features (independent variables) and the corresponding target labels (dependent variable).
• Objective: The objective is to learn a function that can accurately map input features to the correct class label for new, unseen instances.
• Evaluation: Once trained, the model’s performance is evaluated using various metrics depending on the specific classification task. Common evaluation metrics include Accuracy, Precision, Recall, F1 Score, ROC-AUC Curve, etc.

Applications of Regression

1. Predicting Stock Prices

Regression algorithms create mathematical relationships between the stock price and related factors to predict accurate model values using historical data, screening trends and patterns.

2. Sales Forecasting

Organizations planning sales strategies, inventory levels and marketing campaigns can use historical sales data, trends, and patterns to predict future sales. It helps forecast sales in wholesale, retail, e-commerce and other sales and marketing industries.

3. Real Estate Valuation

Establish mathematical equations to predict models that discover the values of real estate properties. An organization can easily determine the property price by depending on the amenities, size and location of the property along with its historical data, including market values and sale patterns. It is widely used by real estate professionals, sellers and buyers to assess expenses and investments.

Applications of Classification

1. Email Spam Filtering

Training is provided to the classifier using labeled data to classify the emails. Filtering of emails can be done by analyzing the two categorical data i.e., spam or not spam. The filtered emails are then automatically delivered to the appropriate class as per the selected features determined in the input.

2. Credit Scoring

Credit scores can be assessed using a classification algorithm. It analyses the history of the client, amount of transactions, loan sanctioned, income, demographic information and other factors to predict the informed decisions of loan approval for the applicants.

3. Image Recognition

The classifier is trained on labeled data, enabling it to predict images based on their corresponding labeled classes. The classification algorithms can automatically categorize images with new content, such as animals or objects, into classes.

Advantages and Disadvantages of Regression 

Let us explore advantage and dis advantage of Regression.

Advantages

• Valuable Insights: Helps to analyze the relationships between distinct variables and achieve a significant understanding of the data.

• Prediction Power: Prediction of dependent variable values with high accuracy using independent variables.

• Flexibility: Regression algorithms, such as logistic, linear, polynomial, and others, are flexible tools used to find or predict a wide range of models.

• Ease in Interpretation: You can easily visualize the analyzed results of regression in the form of charts and graphical representations.

Disadvantages

• False Assumptions: The regression algorithm lies on numerous assumptions, thus resulting in false assumptions in the context of the real world. It includes normality of errors, linearity and independence.

• Overfitting: Regression models may inadequately perform on new and unseen data when they are overly customized for the training data.

• Outliers: Regression models are sensitive to exceptions, thus, can have a significant effect on analyzed prediction results.

Advantages and Disadvantages of Classification 

Let us explore advantage and dis advantage of classification.

Advantages

• Accuracy in Prediction: With fitting training, the classification algorithm achieves high accuracy in the model prediction.

• Flexible: Classification algorithms have many applications like spam filtering, speech and image recognition.

• Scalable Datasets: Easy to apply in real-time applications that can scale up huge datasets easily.

• Efficient and Interpretable: The classification algorithm efficiently handles huge datasets and can classify them quickly, which is easy to interpret. It provides a better understanding of variable-to-outcome relationships.

Disadvantages

• Bias: If the training data does not represent the complete dataset, certain trained data may bias the classification algorithm.

• Imbalanced Data: If the classes in the datasets are not balanced equally, the classification algorithm will favor the majority class and neglect the minority class. For example, in a dataset with two classes, such as 85% and 15%, the classification algorithm will represent the majority class as 85%, leaving the minority class undefined.

• Selection of Features: If the classification algorithms do not define features, predicting data with multiple or undefined features becomes challenging.

Differences Between Regression and Classification 

Let us have a comparative analysis of regression vs classification: 

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When to Use Regression or Classification?

The classification vs regression usage in different domains is stated as follows: 

A. Data types

Data Types used as input are continuous or categorical in regression and classification algorithms. But the target value in regression is continuous, whereas categorial is in the classification algorithm.

B. Objectives

Regression aims to provide accurate continuous values like age, temperature, altitude, shock prices, house rate, etc. The classification algorithm predicts class categories like a mail is either spam or not spam; the answer is either true or false.

C. Accuracy requirements

Regression mainly focuses on achieving the highest accuracy by decreasing the prediction errors like mean absolute error or mean squared error. On the other hand, classification focuses on achieving the highest accuracy of a particular metric applicable to the given problem, like ROC curve, precision and recall.

Conclusion

Understanding the differences between regression vs classification algorithms is crucial for data scientists to solve market issues effectively. Accurate data predictions rely heavily on selecting the right models, ensuring high precision in the results. If you want to enhance your machine learning skills and become a true expert in the field, consider joining our Blackbelt program. This advanced program offers comprehensive training and hands-on experience to take your data science career to new heights. With a focus on regression, classification, and other advanced topics, you’ll gain a deep understanding of these algorithms and how to apply them effectively. Join the program today!

Hope you like the article! In machine learning, understanding the difference between classification and regression is crucial. Classification vs regression involves predicting categories, while regression predicts continuous values. Explore the nuances of regression vs classification for better insights. The difference between classification and regression lies in their objectives. Classification aims to assign data points to specific classes, while regression seeks to predict a continuous target variable. In machine learning, classification and regression are widely used techniques for various applications, such as image recognition, sentiment analysis, and stock price forecasting.