Feature Engineering for Machine Learning Models: Techniques, Examples, and Best Practices

Key takeaways 

Feature engineering for machine learning involves taking raw data and turning it into usable information that machine learning models can comprehend. Here are some important facts to know: 

• The global machine learning market is projected to have a 31.72 percent compound annual growth rate (CAGR) from 2025 to 2031 [1].

• Feature engineering focuses on gathering key variables from data, which are specific to your use case.

• You can work with machine learning models in high-demand careers, such as a machine learning engineer and a data scientist.

Learn more about how feature engineering can help train machine learning models and lead to more accurate predictions. If you’re ready to develop your machine learning skills, enroll in the IBM Machine Learning with Python & Scikit-learn Professional Certificate. You’ll have the opportunity to gain valuable experience with machine learning algorithms, practice training neural networks, and more in as little as three months.

What is feature engineering in machine learning?

Feature engineering is the process of extracting relevant information from raw data that you can then use for machine learning. A “feature” in feature engineering describes the data’s unique attributes. For example, if you were working with point-of-sale data, features within the data could include attributes like the items purchased, amount paid, and payment method. Extracting the most relevant data for your machine learning model is important because it helps avoid overcomplexity and improves the training process, supporting the model in learning to make accurate predictions.

Feature engineering techniques

Your feature engineering strategy can include one or a combination of the following techniques: feature selection, feature extraction, feature transformation, and feature creation. Possessing domain knowledge is especially valuable in feature engineering, so you can understand the best approach to take to focus on the most meaningful data for the task at hand. Each feature engineering technique has a different approach to improving your data:

• In feature selection, you choose a subset of extracted features based on measurable values like the correlation matrix, which allows you to highlight the data that has the greatest predictive power.

• In feature extraction, you can combine variables in order to reduce dimensionality. This is beneficial for the model because it reduces the amount of computing power it takes to train the model, in addition to lowering memory requirements.

• In feature transformation, you are able to alter existing features to optimize the data for future modeling. For example, through a technique called binning, you can transform continuous variables into categorical variables.

• In feature creation, you can create new features within your data. For example, you can take two features and combine them into one, which is useful if variables have little value individually but provide meaningful insights when combined due to their correlation.

Feature engineering for machine learning models in Spark​

Apache Spark is a useful tool for completing feature engineering tasks on your data, with algorithms specifically for feature extraction, feature transformation, and feature selection.

Why the feature engineering process matters and real-world use cases

Feature engineering is important because it improves the efficiency of machine learning, allowing models to direct their learning toward relevant information that will enhance performance when it comes to their designated use case. Feature engineering use cases are visible in industries like health care and finance:

• Health care: Feature engineering helps improve patient health outcomes through a more personalized approach to treatment, as well as detecting diseases quickly. The data preprocessing involved in feature engineering helps to improve the quality of data, leading to better predictions of future health risks.

• Finance: By extracting features from historical data, feature engineering is a useful tool for predicting movement in the stock market. The combination of domain knowledge and feature engineering is powerful for detecting anomalies that affect stock prices, generating information to inform investment decisions.

Feature engineering challenges and best practices

A challenge of feature engineering is that data skills alone aren’t enough, as domain knowledge plays an important role in the process. Domain knowledge is valuable because it allows you to successfully identify the data features that will be most valuable to the model, as well as find opportunities to combine or alter features. Another issue you can often face in feature engineering is working with different data types and formats, as data rarely comes from only one source, and you will need to merge them into one common data set. However, by properly preparing your data with domain-specific details in mind, you can develop robust models.

Deep learning and feature engineering

Deep learning is impacting feature engineering in the amount of time it takes to identify and combine features. For example, using deep learning, you can automate some of the feature engineering process by instructing the algorithm to seek certain features within images that it can combine with other features for pattern recognition. However, for deep learning to work well in this context, you must have large image data sets; otherwise, traditional feature engineering tends to perform better when data is less abundant.

Read more: What is Deep Learning? Definition, Examples, and Careers

Feature engineering vs. data engineering

Feature engineering is the process of extracting relevant attributes from raw data that you can use to train a machine learning model. Data engineering describes the process of building and maintaining the infrastructure that collects and organizes the data so that data scientists can access it.

Getting started in feature engineering

To get your start in feature engineering, look to develop valuable skills like programming with Python. Python is a valuable option since you can access libraries to automate feature engineering tasks like transforming and extracting features. Sci-kit learn is another great library to learn, as it has features to support a wide range of machine learning tasks, including feature engineering.

If you’re interested in pursuing a career in building machine learning models using feature engineering, machine learning engineers and data scientists are in high demand. The global machine learning market is projected to have a 31.72 percent CAGR from 2025 to 2031, making now a great time to grow your skills in this field [1].

Explore our free machine learning resources

To continue learning about managing data and building machine learning models, explore free resources like our YouTube channel. Then, check out the following resources:

• Take the quiz: Machine Learning Career Paths: Explore Roles & Specializations

• Hear from an industry professional: 6 Questions with an IBM Data Scientist and AI Engineer

• Learn Python programming: Python Syntax Cheat Sheet

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