Binary Classification for Beginners

Binary classification is a type of machine learning algorithm used in many industries, such as health care and finance, as well as in web-based applications. It provides powerful insights, including identifying patterns and making predictions based on past data. Overall, binary classification models help businesses make better decisions.

Explore what machine learning algorithms are, how to apply binary classification, and the different professions that use them.

Read more: 10 Machine Learning Algorithms to Know

What are machine learning algorithms?

Think of algorithms like sets of instructions that you give computers to help the machines solve problems. Machine learning algorithms form the basis for chatbots, language translation apps, movie and song suggestions, medical image diagnostics, and what your social media feed shows. 

Machine learning works through algorithms, including binary classification, that allow machines to make predictions based on information without having explicit guidelines. Machine learning algorithms have three categories of functions: descriptive, predictive, and prescriptive.

• Descriptive learning: The machine uses data to explain results or occurrences 

• Predictive learning: The machine predicts outcomes based on input data 

• Prescriptive learning: The machine uses input data to make recommendations about future behaviors.

Machine learning algorithm categories include supervised, unsupervised, semi-supervised, and reinforcement learning.

Read more: Generative AI vs. Predictive AI: What’s the Difference?

Supervised vs. unsupervised learning

In supervised learning, the algorithm learns from a data set with labels. This is similar to having a teacher guide a learner during the learning process. The algorithm uses these labels (the "right answers") to figure out the relationship between the input (the question) and the output (the answer). Once it learns this relationship, the algorithm can apply it to new data outside the training set.

• Unsupervised learning uses unlabeled data. It’s like leaving the learner alone with a bunch of questions but no answers. The algorithm tries to discover the data’s inherent structure, patterns, and relationships by itself.

• Semi-supervised learning combines the two to utilize labeled and unlabeled data during training. Alternatively, you can also use reinforcement learning, which uses rewards and punishments to reinforce desired outcomes.

Machine learning algorithms use classification to assign information to specific groups—or classes. You can have several classification algorithms, including binary and multi-label classification. While binary classification is most common in supervised learning algorithms, where the researcher specifies outcome categories, unsupervised learning algorithms also occasionally use it.  

What is binary classification?

In binary classification, the algorithm predicts one of two possible outcomes. As the name suggests, "binary" signifies two options. The outcome could be a yes or no question, a coin flip resulting in heads or tails, or categorizing an email as spam or not spam. Binary classification provides a framework for many real-world problems that inherently have two possible outcomes.

Types of binary classification algorithms

You can choose between several options when designing a machine learning algorithm for a binary classification problem. Understanding how each algorithm works can help you design your classification system more effectively and determine which classification method makes the most intuitive sense for your program. 

As with any classification method, each binary classification algorithm has advantages and disadvantages that make it more or less suitable for different purposes. When you work with binary classification, it's important to understand how each will relate to your problem and fit your needs. Explore each binary classification algorithm further.

1. Logistic regression

Logistic regression is a statistical model that uses the features of the data to predict probabilities between 0 and 1. The algorithm classifies data points into one of two possible classes (0 or 1) based on a threshold, typically 0.5. This type of algorithm is simpler than many other methods, and many people choose to use it as a starting point before moving to more complex models.

Advantages:

• Easy to implement and efficient to train compared to other models

• Good choice for beginners

Limitations:

• Sensitive to outliers, which can negatively affect the model’s performance

• Assumes a linear relationship between input variables and the logit of the output variable, which might not always be the case

2. Support vector machines 

Support vector machines (SVM) is an algorithm designed for binary classification that finds the hyperplane or line that maximally separates the data points of two classes. It aims to find the hyperplane with the maximum margin and the distance between the hyperplane and the closest class data points.

Advantages:

• Tends to be very accurate and less likely to overfit

• Can handle complex, nonlinear classifications using the “kernel trick”

• Once the model has been trained, you can delete the training data, making it memory-efficient

Limitations:

• Requires a good choice of parameters, which can be computationally intensive

• Can be inefficient with a large number of features

3. Naive Bayes 

Naive Bayes applies Bayes’ theorem while assuming each feature is independent. Essentially, Native Bayes uses training data to calculate probabilities of each class (like “fraud” or “not fraud”) given the features of a new data point.

Advantages:

• Can be trained quickly compared to other classification algorithms

• Can perform well on both large and small datasets

• Handles irrelevant features easily

Limitations:

• Makes a strong assumption about the independence of the features, which is rarely true in real-life data

• Not a sound basis for complex hypotheses or larger, more varied data

4. Decision tree

A decision tree is a flowchart-like structure. Internal nodes are feature tests, and branches are feature test outcomes, and leaf nodes are class labels. Decision trees predict a class for a given input vector. You might think of this as a game of “20 questions” where each question moves the algorithm one step closer to the final classification.

Advantages:

• Requires very little data preparation and handles outliers well

• Easy to understand and interpret, as you can visualize the entire tree structure

Limitations:

• Tend to overfit

• Can become unstable, as small changes to the data can result in a drastically different tree

Read more: Decision Trees in Machine Learning: Two Types (+ Examples)

5. K-nearest neighbor

In this method, also represented as kNN, the algorithm classifies each input into one of the two classes based on the classes of the "k" nearest points in the training data. Basically, you assume the correct classification for a data point is similar to its nearest neighbors. 

Advantages:

• Simple and easy to implement

• No assumptions about the data, making it useful in real-world applications

• No training phase, so it can adapt quickly to changes

• Considered to be stable

Limitations:

• Affected by irrelevant features and the scale of the data

• Requires to store all the training data

Read more: 10 Machine Learning Algorithms to Know

Uses of binary classification

Binary classification has a wide array of uses in different industries. It enables sorting data into two categories or classes, allowing for informed decision-making processes. Some ways you might see binary classification used across industries include:

Medical diagnostics

You could consider binary classification in medical diagnostics similar to going to the doctor’s office to see if you have a particular disease. The doctor examines you and says you have a particular condition or you don’t. This type of binary classification can extend on a much grander scale.

In medical testing, these smart systems analyze health data and say, “This looks like a disease” or “This seems fine.” This approach is great for analyzing medical images, where the system checks whether an image indicates a certain condition or not.

These systems can also check your genes and predict whether you might have certain genetic disorders. You can also train classification algorithms to look for the presence of specific genes, phenotypes, and variations based on training data sets to identify more complex cases of rare diseases.

Financial risk

Binary classification in the financial sector aims to predict financial risk and changes within the market. For example, binary classification can identify risk factors for banking crises in emerging markets. This algorithm can look at factors such as inflation, bank deposits, and bank profitability to decide whether a particular market is at an elevated risk.

Banking institutions have also used binary classification for risk assessment on things like loans or the presence of fraud. When administering a loan, these institutions use systems that decide whether giving you a loan is safe or risky. Based on typical spending patterns, these systems can also check whether a transaction is fraudulent or legitimate. 

Business decisions

Businesses also use binary classification to make smarter decisions and offer better services.

One way they use it is to predict customer behavior. For example, businesses analyze data to determine if a customer will or will not make a purchase based on their previous behavior. You would accomplish this by looking at data such as how long consumers remain on a specific part of a website, recent transactions, and customer satisfaction. By using this knowledge, businesses can better focus their marketing efforts. 

Binary classification also helps businesses decide who is likely to renew a subscription or otherwise remain a customer and who is not. For example, you can use binary classification to predict whether people who play video games are likely to stay customers versus departing based on their data. This distinction can help companies market more effectively to interested consumers rather than those more likely to avoid further purchases.

Careers that use binary classification

Binary classification is a powerful tool to classify information and can benefit professionals in several fields. As a professional, learning binary classification could help you expand your ability to make predictions, analyze organizational data, and make informed decisions about new products and models. Examples of these types of careers include the following. 

1. Financial analysts

Average annual salary in the US (Glassdoor): $79,399 [1]

Job outlook (projected growth from 2023 to 2033): 9 percent [2]

As a financial analyst, you will analyze various information types to help your organization make sound predictions and inform decision-making. You can utilize binary classification to predict market trends, assess investment risks, or detect fraudulent transactions. 

Read more: What Is a Financial Analyst? (+ How to Become One)

2. Marketing analyst

Average annual salary in the US (Glassdoor): $75,427 [3]

Job outlook (projected growth from 2023 to 2033): 8 percent [4]

Marketing analysts use binary classification to analyze customer behavior, predict purchasing patterns and customer loyalty, and tailor marketing strategies to enhance customer engagement and profit from their customers.

Read more: What Is a Marketing Analyst? And How to Become One

3. Network security analyst

Average annual salary in the US (Glassdoor): $100,313 [5]

Job outlook (projected growth from 2023 to 2033): 33 percent [6]

As a network security analyst, you will protect your organization's computer systems, networks, and data. Network security analysts leverage binary classification to build systems that can identify intrusion attacks and enhance the security of digital platforms.

Keep learning binary classification on Coursera

Binary classification algorithms in machine learning provide one of two possible answers to a question. You can use binary classification to solve problems in industries such as cybersecurity, health care, and finance.

You can take several highly-rated courses and Specializations on Coursera to explore binary classification and other machine-learning skills. Consider starting with the Machine Learning: Classification course by the University of Washington, or expand your skills with the entire Machine Learning Specialization.