What Is Machine Learning Classification?

Key takeaways

Machine learning classifications are algorithms that predict information to help businesses make successful decisions, among other applications. 

• Machine learning classification is a supervised learning method that uses two types of learners: lazy learners that memorize the training model and eager learners that build a model based on data.

• Machine learning classification has applications in various industries, from disease prediction in health care to weather condition forecasting in transportation.

• You can use machine learning classification for tasks like finding errors in data, personalizing preferences based on browser history, understanding market research patterns, and more.

Explore the topic of machine learning classification in greater detail to gain a deeper understanding of how machine learning classification works and the value it offers. If you’re ready to start building expertise in machine learning, enroll in the IBM Machine Learning Professional Certificate. You’ll have the opportunity to learn in-demand machine learning skills like machine learning algorithms, predictive modeling, supervised learning, and more in as little as three months. Upon completion, you’ll have earned a career credential for your resume.

What is machine learning?

This subset of AI allows you to detect patterns in data and make predictions based on those patterns using algorithms. Machine learning essentially allows computers to do more tasks on their own. Before AI incorporated machine learning, programs such as rule-based classification were only used for their assigned tasks.

With machine learning, computers can utilize various techniques to process vast amounts of data through algorithms and select the required information. This helps the program learn from the provided data and improve itself. The program forms predictions based on specific examples by following patterns in the data and algorithms.

Read more: How Does Machine Learning Work?

What is classification in machine learning?

Machine learning classification is a method of machine learning used with fully trained models that you can use to predict labels on new data. This supervised machine learning method includes two types of learners that you can use to assign data to the correct category: lazy learners and eager learners.

Lazy learners memorize a training model instead of creating one from the beginning. When making a prediction, lazy learners review the entire data set and look at the closest neighbor. They are slower to make predictions because they wait for an entire data set to be complete.

Eager learner algorithms build a model from all available data before predicting prospective future data sets. By being proactive, eager learners have the preparation in place when sorting data. Eager learning algorithms require more training, but the outcome includes less time to make predictions with more accurate results.

The categories of algorithms for machine learning include supervised, unsupervised, and reinforcement learning. Machine learning classification falls under the category of supervised learning.

Machine learning has the ability to evolve, using one of several algorithms to train it how to learn to improve itself. Choosing the best algorithm in machine learning classification depends on what type of data you input and output, the amount of time and use of human labor going into the training, and the reasons you are using machine learning.

What is supervised learning?

Supervised learning aims to understand data in the correct context when answering a specific question. This method’s main benefit is that it trains algorithms by using labeled data sets. This ensures data accuracy when making predictions. As data is input into the model, the model adjusts the data to fit the algorithm and classifies it accordingly, defining the input and the predicted output.

In cases with unstructured data, supervised learning categories can recognize the data, categorize it, and make predictions. After the proper training, supervised models can not only compare data with proven data but also find any errors in the output and modify or correct them. Humans adjust the model until it can detect patterns independently and conclude the correct labeling when dealing with new data.

In supervised learning, a prediction task can be considered a classification task when the model focuses on the overall meaning of a text, and the target variable is discrete.

Can I use CNN for classification?

Yes, convolutional neural networks (CNNs) excel at object and pattern recognition, making them suitable for image and audio data classification tasks. CNNs comprise several layers that extract features from the input data, and a final fully connected layer performs classification based on these features.

What is machine learning classification used for?

In many industries, you can use machine learning classification to teach machines how and where to group a large amount of data. Once you determine the group for the data by the criteria, it is easier to understand and recognize the data patterns. Once patterns are in place, making more data-driven decisions with increasing precision is easier. Because different machine learning algorithms cluster information together into given categories, classification assigns large amounts of data into individually smaller values to make predictions easier, such as true and false or predefined output labels.

Since machine learning independently determines when new data is put in the system and the relationship between the input and output data according to the classification, the system continually updates itself depending on the patterns it detects. In general, you can use machine learning to:

• Find and flag errors or discrepancies in data entry or transactions.

• Use browser history to personalize shoppers’ preferences.

• Access and understand market research patterns.

• Provide timely and accurate customer service on websites through chatbots.

How classification works in machine learning: Examples

Machine learning classification can be used in a variety of day-to-day applications. In the health care industry, researchers can use machine learning classification to predict new future diseases and whether someone might contract an infection. You could use machine learning classification to categorize documents and analyze learner feedback in education. The transportation industry can even use machine learning classification to predict traffic or road weather conditions.

How to start in machine learning classification

If you are looking to build a career in machine learning, which may include classification, you will likely want a strong foundation in math, science, statistics, data analysis, and programming. You will also need ample knowledge of machine learning tools, including software and techniques. Depending on the specific industry or field you want to pursue, experience or knowledge in that particular area is also essential.

In addition to these core skills, other skills that are important in machine learning and AI careers include the ability to communicate well with others, the capacity to think logically, and time management skills. A candidate must stay up-to-date on the latest technology because machine learning quickly evolves.

One career you could look into is machine learning engineering, in which you could earn a median annual total salary of $159,000, according to December 2025 data [1]. This figure includes base salary and additional pay, which may represent profit-sharing, commissions, bonuses, or other compensation. Machine learning engineers have differing tasks but often design machine learning software, work with data scientists to create models, and develop analysis solutions for images and videos. 

Uncover practical guides for your career journey

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