AI ML

What is Feature Engineering for Machine Learning?

Written by John Terra
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Updated on February 13, 2025

Machine learning has become increasingly popular, although we have yet to tap into its full potential. However, existing machine learning models can always be improved, making them more accurate and efficient.

That’s why we have feature engineering, which we’ll dig into in this blog. We’ll explore the concept of feature engineering for machine learning, including what it is, why we need it, its processes, steps, tools, and techniques, as well as a few feature engineering examples. Stay tuned till the end of the article because we’ll drop some info about an AI ML bootcamp that will get you started in this field.

Let’s begin by answering: “What is feature engineering?”

Overview: What is Feature Engineering?

Feature engineering describes the process of using domain knowledge to choose and alter the most relevant variables pulled from raw data when building a predictive model employing statistical modeling or machine learning. These variables are then used to create features useful for ML models. The ultimate goal of feature engineering and selection is improving machine learning (ML) algorithm performance.

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But what’s a feature? Let’s go back to basics and define the term so we know exactly what’s being engineered.

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What is a Feature?

When discussing machine learning, a feature (alternately known as a variable or attribute) is an individual measurable data point characteristic or property used as input for a machine learning algorithm. A feature can be categorical, numerical, or text-based, representing different aspects of the data relevant to the current problem.

Here’s a breakdown of typical feature types:

Categorical: These features take one of a limited number of values, such as colors (red, green, blue) or gender (female, male, non-binary).
Ordinal: Ordinal features are categorical features with a straightforward ordering, such as T-shirt sizes (S, M, L, XL).
Binary: Binaries are a special case of categorical features containing only two categories: registered voter (yes/no) or on the mailing list (true/false).
Numerical: These features are values with numeric types (int, float, etc.), such as weight, age, and income.
Text: As the name implies, text features have textual data. Textual data typically needs special preprocessing steps, such as tokenization, to transform it into a form that works with machine learning models.

What is the Need for Feature Engineering in Machine Learning?

Feature engineering is used for many reasons, and some of the chief reasons include:

Improve User Experience

Feature engineering aims to enhance the user’s experience with a service or product. We can make a product more efficient, intuitive, and user-friendly by adding new features, which could increase user satisfaction and customer engagement.

Competitive Advantage

We also engineer features to secure a competitive marketplace advantage. By offering unique, innovative features, a product can stand out from the rest of the crowd and attract new customers.

Meeting Customer Needs

Features are also engineered to meet the customer’s evolving needs. By analyzing market trends, user feedback, and customer behavior, we can identify areas where new features may enhance a product’s value and better meet customer needs.

Increasing Revenue

Features can also be engineered to create additional revenue. For example, a newly introduced feature streamlining a supermarket’s checkout process can boost sales, or a feature providing an app with additional functionality generates more upsells or cross-sells.

Future-proofing

Engineering features can also be introduced to future-proof a product or service. Future-proofing means the product won’t fail or become obsolete in the future. By anticipating potential customer needs and trends, we can develop features that ensure the service or product will stay valuable and relevant in the long run.

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Explaining the Feature Engineering Process

The following describes the typical feature engineering process:

Feature Creation

This step involves identifying variables most beneficial for the predictive model, a subjective process that requires human intervention, judgment, and creativity. Existing features are combined through addition, subtraction, multiplication, and ratio, creating new derived features with greater predictive power.

Transformations

Transformation covers manipulating predictor variables to improve the model’s performance. This process includes things like:

Ensuring the model is flexible regarding the variety of data it can ingest
Ensuring the variables are on the same scale
Making the model easier to understand
Improving accuracy
Avoiding computational errors by ensuring that all features are within the model’s acceptable ranges.

Feature Extraction

Feature extraction automatically creates new variables by extracting them from the raw data. This step’s primary purpose is to automatically reduce the data volume into a more manageable set for modeling. Standard feature extraction methods include edge detection algorithms, cluster analysis, text analytics, and principal components analysis.

Feature Selection

Feature selection algorithms analyze, judge, and rank different features to decide which are irrelevant or redundant and should be removed and which are the most beneficial for the model and thus should be prioritized.

Feature Engineering Steps

The following are the commonly accepted feature engineering steps:

Data preparation. Data preparation is the initial step. In this step, the raw data gathered from different resources is prepared and put into a format suitable for the ML model. The data preparation may include cleaning, augmentation, delivery, fusion, ingestion, or loading.
Exploratory analysis. Exploratory analysis, also called exploratory data analysis (EDA), is an essential feature engineering step used mainly by data scientists. This step involves analyzing data sets and summarizing their main characteristics. Different data visualization techniques can be employed to understand sources better and perform ethical data manipulation, find the most effective statistical techniques for data analysis, and then select the best features for the data.
Benchmarking. Finally, benchmarking sets a standard baseline for accuracy to compare every variable from this baseline. This process improves the model’s predictability and reduces the error rate.

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Feature Engineering Techniques

Feature engineering typically employs the following popular techniques:

Binning. Overfitting is one of the most prevalent issues in machine learning. It degrades model performance because of more parameters and noisy data. However, binning can normalize noisy data by segmenting the different features into bins.
Feature Split. As the name implies, feature split is the process of splitting features into two or more parts and performing to create new features. This technique helps algorithms to comprehend and learn dataset patterns better. The process enables the new features to be clustered and binned, extracting useful information and improving data model performance.
Handling outliers. Outliers are deviated values or data points that appear too distant from other existing data points that negatively affect the model’s performance. This technique starts by identifying the outliers and then removing them. Standard deviation can also be used to spot outliers. For example, each value in a space has a definite to average distance. Still, if the value has a greater distance than a particular value, it can be considered an outlier. Additionally, Z-scores can be employed to detect outliers.
Imputation. Feature engineering often deals with inappropriate data, insufficient data sources, missing values, general errors, human interruption, etc. Missing values in the dataset highly affect the algorithm’s performance, so the imputation technique is used to handle them. Imputation handles irregularities within the dataset.
Log transform. Log transform, also known as logarithm transformation, is one of the most used mathematical techniques in machine learning. It helps handle skewed data, making the distribution more approximate to normal after the transformation is finished. Log transform also reduces the effects of outliers on the data.

Feature Engineering Tools

Here’s a small sampling of different tools used in feature engineering.

Alteryx. Alteryx is a data preparation and automation tool that includes feature engineering. It offers a visual interface for constructing data pipelines that can extract, alter, and generate features from diverse data sources.

DataRobot. This machine learning automation platform uses automated machine learning techniques to create new features and choose the best combination of features and models for any given dataset.

Featuretools. This is a Python library that allows automatic feature engineering on structured data. It can extract features from multiple tables, such as CSV files and relational databases, and then generate new features based on user-defined primitives. Primitives are statistical functions applied to transform the data located in the entity set.

TPOT. TPOT (Tree-based Pipeline Optimization Tool) uses genetic programming to search for the best mix of features and machine learning algorithms for any given dataset.

Feature Engineering Examples

Here are a few examples of feature engineering that can help make the concept easier to understand.

Body mass index (BMI). BMI is calculated using body weight and height and serves as a fill-in for a highly challenging characteristic: the lean body mass proportion.

Property prices. Let’s say you’re a realtor who uses a chart to show the values of six properties, broken down by square foot.

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