Classification vs. Regression Algorithms in Machine Learning

“Machine Learning” is one of the most trending buzzwords. It is predominant in every industry sector as it empowers various organizations with innovative solutions to automate and increase the efficacy of products by reducing human intervention. You might have heard about the applications of weather forecasting, spam classification, or stock price prediction applications, so what exactly do these applications use? Yes, you are right. They are built using Machine Learning algorithms. These algorithms majorly fall into two categories - supervised algorithms and unsupervised algorithms. While supervised algorithms comprise data with labels, unsupervised algorithms have unlabelled data. 

Build Classification Algorithms for Digital Transformation[Banking]

Downloadable solution code | Explanatory videos | Tech Support

Start Project

Machine Learning Classification vs. Regression 

In this blog, we will be looking at the differences between two of the most prominent supervised machine learning algorithms - Classification vs. Regression. 

What is Classification? 

Classification is a supervised machine learning algorithm that predicts specific discrete values (categories or classes) to which the input belongs. Majorly, there are three classification problems types: binary, multi-class, and multi-label. Let’s understand each of them with examples.

1. Binary Classification: This classification problem can fall into two classes. For example, to classify an email as spam or not spam, we can assign label 0 for spam and label 1 for not spam, which will likewise convert the output probabilities to either one of these labels, making it a binary classification problem. 

2. Multi-Class Classification: Here, classification can be from more than two classes. Suppose we have the past labeled data on the weather of a place that labels individual days into three classes - sunny, cloudy, or rainy. In that case, we can train a model and use it to predict tomorrow’s weather which can fall into any of these mentioned categories. This is an example of multi-class classification. 

3. Multi-Label Classification: Let’s consider an example of semantic tagging. In semantic tagging, the idea is to analyze some text information and predict the content categories of the text. For instance, for a news article based on government regulations on Covid-19, the information can be classified under “Political,” “Law & Government,” and “Healthcare” categories. This means if the same input can have either one, two, or more categories assigned to it, it becomes a multi-label classification problem. 

New Projects

LLM Project to Build and Fine Tune a Large Language Model

Build an ETL Pipeline for Financial Data Analytics on GCP-IaC

Loan Default Prediction Project using Explainable AI ML Models

Predictive Analytics Project for Working Capital Optimization

Stock Price Prediction Project using LSTM and RNN

dbt Snowflake Project to Master dbt Fundamentals in Snowflake

LLM Project to Build and Fine Tune a Large Language Model

Learn to Build an End-to-End Machine Learning Pipeline - Part 1

Flask API Big Data Project using Databricks and Unity Catalog

Stock Price Prediction Project using LSTM and RNN

What is Regression?

Regression is a supervised machine learning algorithm used to predict the continuous values of output based on the input. There are three main types of regression algorithms - simple linear regression, multiple linear regression, and polynomial regression. Let’s have a look at each of them with examples. 

1. Simple Linear Regression comprises a mapping function that models the linear relationship between a dependent variable (to be predicted) with one independent variable. For instance, let’s consider the housing prices of a locality are only dependent on the area. So, given the area of any new locality, one can predict the housing prices using the trained model on past data. 

2. Multiple Linear Regression: This type of regression consists of a relationship between multiple independent variables and a dependent variable. For example, the ratings of a restaurant are dependent on the quality of food and the ambience, service, and location. So, in such a problem, multiple independent parameters linearly affect the dependent parameter (rating of a restaurant). 

3. Polynomial Regression: This algorithm maps the non-linear relationship between the independent and dependent variables. The mapping consists of multiple powers of an independent variable, which makes the equation non-linear. For example, you can use this type of algorithm to predict the number of Covid-19 cases. As the increase or decrease in cases is not linearly related to the number of people wearing masks, polynomial regression can map the non-linearity and predict. 

Here's what valued users are saying about ProjectPro

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data. In each learning path, there are many customized projects with all the details from the beginner to...

Jingwei Li

Graduate Research assistance at Stony Brook University

Having worked in the field of Data Science, I wanted to explore how I can implement projects in other domains, So I thought of connecting with ProjectPro. A project that helped me absorb this topic was "Credit Risk Modelling". To understand other domains, it is important to wear a thinking cap and...

Gautam Vermani

Data Consultant at Confidential

Not sure what you are looking for?

View All Projects

Classification vs. Regression - Data

In classification, the data comprises discrete labels or classes such as “0”, “1”, “cat,” “dog,” “flower,” “bird,” etc. The output probability of a classification model is supposed to be mapped to the defined classes based on a certain threshold. The idea for classification is to find the best decision boundary to segregate the samples into discrete categories. On the other hand, the data for regression comprises continuous values of labels. The output of a regression model is a continuous variable. For regression, the idea of modeling is to find the best fitting line which can accurately predict the output. 

Classification   Regression 

Classification vs. Regression - The Different Machine Learning Algorithms

There are multiple machine learning algorithms under the umbrella of classification and regression. Here, we will discuss the core differences between some of the most commonly used algorithms in classification and regression. 

• Linear Regression vs. Logistic Regression

Linear regression attempts to create a mapping function to establish a linear relationship between the independent and dependent variables. Mathematically, you can describe it with the equation of a line: 

Y = θâ‚€x + θ₁, where,

θâ‚€ is the slope of the line,

θ₁ is the intercept, 

x is the independent variable, 

Y is the dependent variable

Here, the problem is to find the optimal values of θâ‚€ and θ₁ to form the mapping function. There are two strategies to do this - Gradient Descent and Normal Equation. 

i) Gradient Descent: The gradient descent rule is used to continuously update and optimize the values of θâ‚€ and θ₁ to reach the local minima. The update rule is as follows: 

According to the update rule, we continuously update the values of θâ‚€ and θ₁ by calculating the partial derivatives of the loss function J with a learning rate of 𝛼. Here, the loss function is Mean Squared Error, and the learning rate determines how the updates or learning should happen. When this equation converges, we can obtain the best suitable values of θâ‚€ and θ₁. 

ii) Normal Equation: For large datasets, gradient descent is preferred because of computational profits; however, when the dataset is small, we can alternatively opt for a normal equation which is a non-iterative algorithm. The hypothesis for a normal equation goes like this - 

X refers to the input data matrix, and Y is the matrix of labels. The parameters obtained using this equation will be optimal. Once the parameters θâ‚€ and θ₁ are obtained, the mapping function of linear regression will be complete and can be used to predict the output of unseen inputs. 

Logistic Regression is a classification algorithm that predicts discrete values as outputs, unlike linear regression. Mathematically, the hypothesis for logistic regression is: 

P = 1 / (1 + e—(θâ‚€ + θ₁x) 

This function is called the sigmoid function. Here, the values of θâ‚€ and θ₁ are predicted just like in linear regression. As the output of logistic regression is a probability score, the sigmoid function is used to map the output between 0 and 1. It will represent an S-shaped curve. Since linear regression is not a good choice for multiple outliers, logistic regression can accommodate such scenarios. 

For the loss function, you cannot apply Mean Squared Error (MSE) used in linear regression  here as the target variable P is non-linear. If MSE is used here, then the graph would not be convex. Therefore, we use the concept of Maximum Likelihood Estimation to obtain the logistic loss function. In Maximum Likelihood Estimation, we are supposed to find those parameters that will help maximize the likelihood function. Here, the likelihood function is as follows: 

We will again use the gradient descent approach for the optimization task. Gradient descent tries to minimize the value of the loss function, but since we have to maximize this likelihood function, a negative sign is introduced in the beginning. By incorporating this loss function in the gradient descent update rule, the final values of parameters can be obtained. 

• Support Vector Machine vs. Support Vector Regression

Support Vector Machine (SVM) is a classification algorithm that aims to find a hyperplane for segregating the classes. The hyperplane is formed so that it tries to maximize the separation between two classes (the perpendicular distance) while minimizing the intra-class distance. It’s objective function looks like: 

Source: Web

Here, w * x - b >= 1, for y = 1 

Else, w * x - b <= 1, for y = -1 

Support Vector Regression (SVR) is an extension of SVM algorithm to incorporate the continuous nature of values. Like SVM, SVR also deals with predicting a hyperplane to suit all the points in data. The only difference here is that we want to determine a line at a distance not more than a particular epsilon value of epsilon 𝜺. It will be an equidistant hyperplane with the points as close as possible. SVR attempts to perform a linear regression-like algorithm with a non-linear mapping function in a high dimensional space. It’s objective function will look like: 

y - w * x - b <= 𝜺, 

w * x + b - y <= 𝜺 

• Decision Tree Classification vs Decision Tree Regression 

Decision trees can be used for both the tasks - classification and regression. In a decision tree for classification, the leaf nodes comprise binary values such as “True,” “False,” or “Yes,” “No.” The first node is called the root node. After splitting the root node, the obtained nodes are called the decision nodes. Naturally, the next question is, how do we decide the root node or the decision node? To understand that, let’s first explore the concept of entropy.

Entropy is the amount of randomness present for a particular feature. For example, in a group of 10 people, if we vote for how many people like pizza, we get the responses as six people like pizza, and the other four don’t. In such a scenario, we can say that impurity is present because the negative class is also present, and there is no clear split. To construct a decision tree, the amount of impurity should be zero. Hence we require splits which can result in creating leaf nodes. Mathematically, the formula of entropy is:

Source: Web 

P+  refers to the probability of a positive class, and P– is the probability of negative class. 

The ultimate aim of a decision tree is to reduce the entropy (randomness) in the data. Once the entropy is calculated, the next step is to identify whether the entropy of a particular node has decreased compared to the parent node. To figure out that, let’s explore the concept of information gain. 

Information gain measures the amount of entropy reduced for a particular node with respect to the parent node. Mathematically, information gain is represented as 

In the equation, E(Y) refers to the entropy of parent node. E(Y|X) calculates the entropy of node X with respect to node Y. 

Given a certain number of features based on which the decisions are made, information gain is calculated for each of those features, and the feature with the maximum amount of entropy reduction is then chosen for the root node. Similarly, the branching continues on the other features. 

The concept of decision trees for regression is quite analogous to the decision trees for classification. For regression, the nodes' values will comprise continuous numerical values instead of discrete binary choices. The final aim of the decision tree remains the same, i.e., to reduce the entropy of the entire dataset. However, for classification, the variation in the entropy cannot be calculated using the information gain formula described earlier as the values are continuous. Hence, you can use the Mean Squared Error (MSE) function to obtain the deviation of randomness in the nodes. 

• Random Forest Classification vs. Random Forest Regression

Random forest can also be used for classification and regression tasks. For classification, the outputs would be discrete, while they would be continuous for regression. Random forest is built using ensemble learning. There are two divisions under ensemble learning - bagging and boosting. Random forest uses the bagging approach, where the power of multiple predictors (models) is combined to give the final output. Below is how the random forest algorithm works -

1. The dataset is divided into many subparts. Each of these subparts is fed to a different model. This is done to reduce the chances of getting similar learning and predictions from every model. 

2. These models are essentially individual decision trees. 

3. Each model will provide an output, and the final output will be decided based on the maximum votes obtained from voting. 

This strategy likely improves generalization performance, less overfitting, and more confident predictions. 

Classification Loss Functions vs. Regression Loss Functions

Let’s look at some of the most used loss functions for regression -

1. Mean Squared Error / L2 is the sum of squared distance between the predicted and the target variables. This loss function is specifically helpful in case of small errors for better convergence. 

2. Mean Absolute Error / L1 is the sum of the absolute distance between the predicted and the target variable. This function is suitable when the error gradients are very large. Otherwise, MSE is a better choice. 

3. Root Mean Squared Error: 

It takes the square root of the sum of squared distance between the predicted variable and the target variable. As MSE extremely penalizes large errors, RMSE can bring a better correlation between both variables. 

4. Huber Loss: 

This is called Smooth Mean Absolute Error loss. It addresses the disadvantages of Mean Squared Error and Mean Absolute Error. It is comparatively less sensitive to outlier cases and is also differentiable at 0. 

Now, let’s look at some of the most prominent loss functions for classification. 

1. Binary Cross-Entropy:

This loss is used for binary classification problems. It tries to measure the difference in entropy (randomness) between the predicted class and the actual class. 

2. Categorical Cross-Entropy:

This loss is used for multi-class classification problems. One hot-encoded input is required to feed into the loss function. This function works well with many classification problems and is most used in the industry. 

3. Hinge Loss: 

This loss focuses on penalizing incorrect classifications. It is majorly used in Support Vector Machines. 

4. Squared Hinge Loss: 

This loss is beneficial to penalize large errors, simply a squared version of the normal hinge loss. 

Other evaluation metrics for classification are - precision, recall, and f1-score. 

Classification vs. Regression - Which is Better?

Classification and Regression, both being supervised algorithms, require labeled data for training. The type of labels is different for both of them. These algorithms are a core part of machine learning; hence a detailed knowledge of them is essential for success in the industry. Last but not least, the best way to develop a complete understanding of machine learning is by implementing projects. So, do check out the awesome end-to-end solved machine learning projects that can help you enhance your learning and knowledge about these algorithms.

About the Author

ProjectPro

ProjectPro is the only online platform designed to help professionals gain practical, hands-on experience in big data, data engineering, data science, and machine learning related technologies. Having over 270+ reusable project templates in data science and big data with step-by-step walkthroughs,

Meet The Author