Glossary term

ML Classification vs Regression

Picture this: you’re trying to predict how a student will perform on an exam using a machine learning algorithm. The only catch is, you’re not sure which algorithm will be most effective. 

The two most popular types of ML algorithms are classification and regression algorithms—while both are supervised algorithms (meaning that, for both types of problems, data scientists provide input, output, and feedback to build the model), their functions differ slightly from one another. 

Classification models are used to predict categorical discrete values such as blue or red, true or false, and so on. Regression, on the other hand, is used to predict numerical or continuous values such as home values, market trends, and so on. If you take the student example from above, a classification algorithm would predict if a student will pass or fail an exam, while a regression algorithm would predict the percentage mark they receive. 

In this guide, we’ll explore different types of classification and regression algorithms, their top use cases, and how you can apply them to your organization. 

Machine Learning Classification vs Regression Explained 

Let’s explore the key differences between each type algorithm and a few examples of each. 

What Is Machine Learning Classification? 

A classification algorithm is a predictive model that divides the input dataset into multiple categorical classes or discrete values. The derived mapping function (f) of the classification model helps in predicting the category or the label of the given input variables (x). 

For instance, if you’re provided with a dataset about different homes on the market, and you want to predict their sale price, a classification algorithm can predict if the home will predict above or below the estimated value. 

Here are a few common ML classification algorithms: 

Decision Tree Classification 

In decision tree classification, the data is split based on certain parameters at each junction. Each node of the tree represents a test case for a particular attribute, and every branch from that node is a possible value for that attribute. 

For instance, say you want to predict if an individual is healthy, given their physical activity level, general health, diet, etc. The decision tree will include nodes with questions like, “Do you exercise?” and “Have you had any recent health issues?,” and the leaves will represent outcomes such as healthy or unhealthy. 

K-Nearest Neighbors 

K-nearest neighbor uses training datasets to identify the nearest point in the training data compared to a given observation point. How similar the features of the new dataset are compared to the training data determines the outcome. 

The main aim of this algorithm is to find out how likely it is for a dataset to be part of a specific group. K-nearest neighbors can be used by banks to predict whether a certain person is fit for loan approval or not by determining if they have similar characteristics to defaulters. 

Logistic Regression 

Despite its name, logistic regression is used to predict a binary outcome such as whether a certain event occurs or does not occur. For example, logistic regression can be used to predict whether a political candidate will win or lose an election. 

This algorithm analyzes the independent variables to determine which of the two categories the outcome falls into. 

Random Forest 

The random forest classifier is an extension of a decision tree. First, you construct many decision trees from a training dataset, then you use majority voting and select the final outcome as the most frequent one. Random forest works on a wisdom of the crowds principle. If you’re trying to determine if an email is spam or not, and five out of six decision trees in your random forest say it is spam, you would classify it as spam. 

Naive Bayes 

The naïve Bayes classifier is a type of Bayesian modeling that calculates conditional probability based on previous knowledge, and the naïve assumption that every feature is independent of each other. For example, a fruit might be considered an orange if it’s orange in color, round, and about 3 to 4 inches in diameter. 

While most ML approaches depend on large amounts of data, naïve Bayes performs well even when you’re dealing with a small dataset. 

What Is Machine Learning Regression? 

The main goal of a regression algorithm is to identify the mapping function from the input variables to the continuous or numerical output variable—a regression technique is often used to predict market trends, home prices, etc. 

In weather forecasting, a regression algorithm is trained on past data (what the weather looked like this time last year and the year before), and once the training is done, it can easily predict the weather for the coming days. 

A few of the most common machine learning regression algorithms are: 

Simple Linear Regression 

Simple linear regression uses a mapping function that estimates the relationship between a dependent variable with one independent variable using a straight line. For instance, considering the prices of houses in a certain area are only dependent on that area, one can predict the housing prices using a trained model on historical data. 

Polynomial Regression 

Polynomial regression is used to model a non-linear relationship between the dependent and independent variables. The mapping function includes multiple independent variables, which makes this a non-linear equation. For instance, polynomial regression is used to predict the spread rate of infectious diseases such as Covid-19. 

Multiple Linear Regression 

Multiple linear regression expands upon simple linear regression and predicts the relationship between two or more independent variables and a dependent variable. For example, the ratings of a restaurant are dependent on the service, food quality, location, and ambiance. In this, multiple independent values (rating factors) affect the dependent value (the overall rating). 

Support Vector Regression 

Support vector regression is a supervised learning model used to predict discrete values. This type of regression can be placed under both linear and non-linear models in machine learning. It utilizes non-linear kernel functions, such as polynomials, to determine a viable solution for the non-linear models. It is used in predicting patterns in the stock market, image processing and segmentation, and text categorization. 

Use Cases for Classification 

Machine learning classification algorithms can be used in different scenarios—here are a few of the most popular use cases. 

Email Spam Detection 

Classification algorithms, specifically the Naïve Bayes classifier, help to automatically classify words based on the likelihood that they have to appear in a spam email message. Words like “free,” “offer,” and “deal” are likely to be classified as spam. 

Determining Loan Worthiness 

To predict and determine a candidate’s ability to pay back a loan, a classification algorithm estimates the applicant’s eligibility based on features such as account balance, annual income, and how quickly they paid back previous loans, which helps credit lenders grant loans with more confidence. 

Biometric Identification 

Supervised learning has been helpful in several biometric apps with the help of classification algorithms. Facial recognition uses decision trees to study images and accurately identify whose facial features are in the picture. Speech emotion recognition software uses support vector machines to detect if someone’s speech conveys emotions of anger, hurt, happiness, etc. 

Use Cases for Regression 

Regression is used constantly in business as well. Here are a few use cases for inspiration: 

Trend Forecasting 

One of the most important applications of regression algorithms is forecasting trends in finance, real estate, gas prices, etc. Regression techniques can be used to predict the success rate of an upcoming marketing campaign, traffic patterns impacting product deliveries, and more. 

Behavioral Analysis 

A regression algorithm can help retailers analyze customer behavior to determine the peak times when customers visit the shop. Using these results, business owners can make sure they allocate staff properly, stock their shelves on time, and provide the best customer experience. 

Error Correction 

Making the best decision for your organization is as important as optimizing business processes. Machine learning regression algorithms give business leaders visibility into how decisions will impact their business down the line, and even allow them to correct already implemented decisions they might want to change. 

Start Making an Impact with Machine Learning Today 

Don’t waste your time barking up the wrong tree, or in this case, experimenting with a less effective machine learning algorithm. 

Choosing the right machine learning algorithm is critical to get the results you need and can often be the difference between a machine learning success story, and a machine learning flop. Understanding the difference between classification and regression algorithms is the first step, but it’s definitely not all there is. 

Learn more about common machine learning algorithms and even more impactful use cases!

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