Machine Learning Classification

One of the most common applications for Machine Learning models is classifying existing data points into different categories. This is then used to determine a wide range of information such as customer activity, anomalies, and automatically cataloging information.

But what exactly is machine learning classification, and what can it do for you? 

In this article, we walk you through everything there is to know about machine learning classification and how you can use it to your advantage.

What Is Machine Learning Classification?

Simply put, classification is the process of categorizing data points according to a set of parameters such as similarities, differences, and features in a subpopulation. 

This supervised learning concept takes a set of data and groups it into classes based on a model that describes the relationship between each data point and its class.

Classification in machine learning has a wide range of applications in predictive analytics, including speech recognition and document classification. It is often used in combination with unsupervised learning concepts such as clustering to form more complex solutions, mainly when applied to big data projects.

Why Is Classification In Machine Learning Important?

Classification is one of the first concepts most people encounter with data, and it is used in a wide range of professional fields across industries, including finance, retail, healthcare, and more.

It is used to categorize data into groups by labeling, based on elements that are known about each individual data point, including for example

• past behaviors, 
• transactions, 
• customer feedback, 
• and email communications. 

It also allows you to sort information by relevance to determine additional information about each individual data point.

For example, an insurance company can use classification to automatically determine the likelihood of a customer filing a claim by combining various variables such as 

• age, 
• gender, 
• and driving record. 

Analysts can use this new information to make more informed decisions about the customer's future interaction with the company.

Another example is Sofware-as-a-service (SaaS) company. They can use classification to determine likelihood of their sales leads to convert into paying customers. Predictive lead scoring helps sales reps focus on prospects most likely to become customers. It's an essential part of sales enablement and is used to prioritize leads who are more likely to convert. 

What Are The Benefits Of Machine Learning Classification?

The benefits of using classification in machine learning are numerous. 

The most apparent advantage is that it allows you to automate the sorting and categorization of information so you can spend more time working on what matters most, using the data effectively.

Classification is also adept at sifting through large datasets to bring even the most minor elements to light so you can make better business decisions. 

Lastly, classification can be used to reinforce your existing business processes through natural language processing. This is done by integrating it with technology to classify data based on specific keywords or phrases. This means you can develop semantic systems that help automate the process of gathering information from your customer base.

Classification Models

There are many different classifiers you can use to classify a dataset. These include, to name a few,

• Logistic regression, 
• decision trees, 
• K-nearest neighbors, 
• and Naive Bayes. 

They all work similarly by analyzing the data and deciding on which individual elements make up a specific subpopulation (class).

Decision Trees

A decision tree is typically a hierarchical data structure used to classify a dataset into separate subpopulations. This method is commonly taught in school and can be used to solve simple classification tasks, but it makes more complex classifications difficult due to its rigid logic.

It works by splitting data into groups based on certain criteria such as age, gender, and income range. It then applies a series of rules or "paths" that need to be taken in order to reach the final subpopulation.

K-Nearest Neighbors

K-nearest neighbors is a type of classifier that identifies the most likely match for a given data point. This produces a set of classification rules that you can use to determine which subpopulation each data point belongs to. The more similar the data points are, the more similar their probability of being classified correctly is.

Naive Bayes

Naive Bayes is a type of classifier that uses a statistical formulation to identify individual estimates in a dataset. The statistic helps it determine the probability of a specific data point being classified correctly.

Unlike other models, it assumes that features are unrelated. Most commonly used in object recognition, data needs to meet a specific set of requirements before it's classed into a subpopulation.

What Can Classification In Machine Learning Do For Me?

Machine learning classification is a great way to categorize large amounts of data and get a better understanding of your customer base. 

It can also help automate tedious tasks such as sorting, cataloging, and editing data so you can free up time to work on other important business objectives.

Take a look at its most common applications below:

Spam Detection

By using machine learning classification, it is possible to assess the probability of sending spam by combining data from a number of attributes such as previous behaviors, geographic location, and time of day. This information can automatically sort email messages into different categories allowing you to identify those that must be checked for spam before they are sent on.

Alternatively, this technology is also used by most email providers to sort out your spam folder. When an email comes in, machine learning classification algorithms are used to identify the likelihood that it is spam mail.

Recognition

From recognizing handwriting to voice, machine learning classification is often used for biometric systems that identify people based on various features, including facial structure, hand position, and gait. These can then be used to verify identity for applications like digital signatures and voiceprints.

For example, in handwriting detection software, machine learning classification models take single characters and cross-reference them with existing characters in the database. If it matches existing data, then you're good to go. Otherwise, it's tagged as an anomaly.

Identifying Customer Churn

Most companies use email marketing to keep customers up-to-date with the latest sales promotions and special offers. This is an effective way to retain customers, but it can also be costly since uninterested people will often unsubscribe, which costs you money.

But machine learning classification can help identify customer churn by cross-checking personal information against your existing customer lists. Active customers are much more likely to respond, so this helps improve response rates for future campaigns. Customer Churn Prevention models can be highly helpful in customer retention efforts.

Sales Lead Scoring

This method uses data from different sources and streams the results in real-time to automate lead scoring decisions.

Generating leads is the lifeblood of your business. Your ability to prioritize and follow up on the right ones can significantly impact your bottom line. 

Machine learning classification algorithms can analyze lead quality so sales teams can focus on the insights they provide. 

Lead scoring is a process that quantifies the engagement of a prospective customer with your company. It can be based on anything from how often they open emails to how many pages they visit. The point of lead scoring is to help salespeople prioritize which leads are worth pursuing.

Conclusion

Machine learning classification is a powerful tool that can help assess the probability of a data point being classified correctly. It's also great for automating repetitive tasks so you can be more productive and enhance your business processes.

If you don't have in-house expertise and a data science team, you should consider using a no-code machine learning tool like Graphite Note for your next classification project.

By understanding its applications and potential benefits, you can choose the proper classifier that fits your requirements. And by using it in conjunction with your data, you'll better understand customer behavior and improve business operations.