What Is Feature Engineering? Techniques, Examples, and ML Use Cases
By
Samantha Cox
•
Jun 30, 2025
Feature engineering is all about turning raw data into something meaningful; something your machine learning model can actually work with. It’s one of the most important steps for improving model performance. In this article, we’ll walk through key techniques, practical examples, and how it’s used in real-world scenarios. You’ll come away with a solid understanding of how to create, transform, and select the right features to boost your model’s accuracy. And if you’re a recruiter looking for talent that knows how to make data truly work, Fonzi AI connects you with engineers who specialize in feature engineering and other high-impact ML skills.
Key Takeaways
Feature engineering is the process of transforming raw data into meaningful features that improve machine learning model performance, including techniques like one-hot encoding and feature scaling.
Effective feature engineering significantly enhances model accuracy and interpretability, often considered more critical than the choice of algorithm itself.
Core processes in feature engineering include feature creation, transformation, extraction, selection, and scaling, with iterative testing and domain knowledge being essential for maximizing model effectiveness.
Understanding Feature Engineering in Machine Learning
Feature engineering refers to the art of transforming raw data into actionable insights. It encompasses various feature engineering techniques for converting raw data into meaningful features that improve model accuracy. Efficient use of features is key to enhancing model performance in feature engineering. Common techniques include:
One-hot encoding
Feature scaling
Creating interaction features
Among others.
Expert feature engineering is distinguished by the ability to extract nuanced patterns and enhance understanding of complex relationships. This guide will help you navigate the core processes and techniques of feature engineering, ensuring you can implement these strategies effectively to boost your machine learning models.
What Are Features?
A feature is an individual measurable property or characteristic of a data point used as input in a machine learning model. These are the input variables that algorithms utilize to make predictions. A feature can be defined as any measurable input utilized in a predictive model, including feature values. It plays a crucial role in the model’s effectiveness. Features can be:
Numerical
Categorical
Time-series
Text-based
Numerical features represent data in numbers, including continuous quantitative variables such as:
Age
Height
Weight
Income
Understanding these different types of features is crucial for effective feature engineering in machine learning.
Importance of Feature Engineering
Effective feature engineering can lead to significant improvements in model performance. The process of feature engineering is often deemed more crucial than the choice of algorithm itself. Quality features can enhance not just accuracy but also the interpretability of machine learning models. Feature engineering is essential for maximizing these benefits.
Creating meaningful features can significantly enhance model performance and add value to existing data. This process is essential for transforming raw data into features that significantly boost the accuracy and reliability of your models.
Core Processes in Feature Engineering
Feature engineering includes creating new features from raw data and transforming existing ones for model input. The goal is to create informative and relevant features for training models. The main processes involved in feature engineering include:
Feature Creation
Feature Transformation
Feature Extraction
Feature Selection
Feature Scaling.
Feature engineering is an iterative process that often requires experimentation and testing. Utilizing domain knowledge during feature creation can significantly enhance model performance. Feature engineering involves creating significant features that improve the overall effectiveness of the model.
Feature quality largely determines model success, making feature engineering a crucial step in the machine learning pipeline.
Feature Creation
Creating new features provides more logical insights into the model, allowing for better predictions. Features can be derived from various sources, including raw data processing and domain-specific knowledge, with types varying widely. For example, calculating a house’s age by subtracting the year it was built from the current year is a way to create features, including creating interaction features and a new feature.
Mathematical operations used in feature creation include aggregations like mean, median, mode, sum, difference, and product. A well-engineered feature can often uncover hidden patterns that raw data alone might not reveal. For example, categorizing days of the week into a Weekend feature with binary values can provide insights into sales trends.
Feature Transformation
Feature transformation is the process of modifying features to enhance their suitability in machine learning models. Poor-quality features can negatively impact model performance, and transformation ensures that models can learn effectively from data. Methods of feature transformation can include:
Exploring and testing features to identify valuable transformations
Creating date-derived features for predicting outcomes
Examining relationships between features like Length or Breadth and prices
Transforming features ensures models work with the most relevant features and meaningful data, improving model performance overall.
Feature Extraction
Feature extraction involves creating new features from existing ones. New features can be generated by transforming, combining, or aggregating existing ones. Variable transformation techniques, like logarithmic transformation, are used to normalize skewed data.
Logarithmic transformation compresses larger numbers and expands smaller numbers to normalize heavy-tailed distributions. This technique is primarily used to convert skewed distributions into normal or less-skewed distributions, making the data more suitable for machine learning models.
Feature Selection
The success of machine learning applications is highly influenced by the features employed. Their quality and relevance play a critical role in determining outcomes. Feature selection techniques, such as correlation analysis and principal component analysis, help in identifying the most impactful variables for model performance. Choosing relevant features is critical for optimal model performance.
Iterative feature selection involves adding or removing features based on mutual information scores with model residuals. Exploratory Data Analysis (EDA) is crucial for identifying relevant variables and understanding data distributions, which guides the feature creation process.
Regularly assessing feature contributions through model validation ensures optimal model’s performance.
Feature Scaling
Feature scaling is the process of adjusting input values of features to ensure that they contribute equally to the model, enhancing its effectiveness. Common methods of feature scaling include Min-Max Scaling and Standardization, which transform data into a similar scale.
Min-Max Scaling transforms values to a range between 0 and 1, while Standardization adjusts values to have a mean of 0 and a variance of 1. When scaling sparse data, caution should be exercised as it may lead to additional computational overhead and complexity.
Common Techniques in Feature Engineering
Feature engineering encompasses various processes, including:
Feature creation
Engineering is the process
Extraction
Exploratory analysis
Benchmarking
Techniques for generating meaningful features from raw data are essential for improving model performance. Creating new features by combining or transforming existing ones can lead to improved model performance.
Generative techniques, such as feature creation, utilize domain knowledge or data patterns to generate features as additional variables. Feature creation is essential for improving model performance significantly. Feature-engine provides advanced functionalities for handling data issues such as missing values and outliers.
Handling Missing Values
Handling missing values in data is critical to preventing distortion of model performance. Missing numerical values are typically handled by replacing them with the mean or median of the corresponding values. For categorical variables, missing values can be replaced by the most common value or the highest category, or by using the ‘Other’ category if values are evenly distributed. Additionally, it is essential to handle missing data effectively, including handling missing data, to ensure accurate analysis.
Imputation methods can be categorized into numerical and categorical depending on the type of missing data, which can affect overall data quality.
Encoding Categorical Variables
Categorical features are discrete features that can take on a limited number of values, which can be either binary or non-binary. Categorical variables need to be converted into numerical representations. This is necessary because machine learning models cannot directly process categorical data. Common encoding methods for categorical features include one-hot encoding, label encoding, and ordinal encoding, especially when dealing with categorical values.
Scikit-Learn provides various tools for encoding categorical variables, such as OneHotEncoder and LabelEncoder, as well as other techniques like DictVectorizer and FeatureHasher.
In addition to basic encoding, other techniques like mean encoding, count encoding, and target encoding can be utilized to convert categorical features into numerical values.
Handling Outliers
Outliers are unusually high or low values that are unlikely to occur normally. For instance, in a salary dataset where most salaries are between $90K and $120K, an outlier could be a value like $400K or $10K. It is important to handle outliers to prevent them from adversely affecting predictions. Handling outliers can significantly impact accuracy, especially for sensitive models like linear regression.
One common technique for handling outliers is capping, which involves setting maximum and minimum values to an arbitrary or distribution-based value. If outliers are removed, they can be considered as missing values. These missing values can then be replaced through imputation.
Capping outliers retains data within a specified range to minimize their impact on analysis. It’s crucial to handle outliers before model training to ensure accurate and reliable predictions.
Normalization and Standardization
The common techniques for scaling in feature engineering are normalization and standardization. Feature normalization is the process of scaling values to a range between 0 and 1, often using min-max scaling. Standardization involves adjusting data so it has a mean of zero and a standard deviation of one, accounting for variance in the feature.
Feature scaling is an essential process in feature engineering that ensures different features contribute equally to the model. Scaling features prevent some from dominating others due to magnitude, leading to more balanced models.
Creating Interaction Features
Feature extraction involves:
Simplifying the data by identifying useful information without losing significant relationships.
Creating derived features that enhance predictive accuracy by combining or transforming existing features.
Using interaction features, which are combinations of existing features that can highlight hidden relationships within the data.
Interaction features reveal overlooked relationships, leading to deeper insights and improved model performance. For example, combining features like Length and Breadth to capture complex relationships and create an Area feature can provide more insights into property prices.
Advanced Feature Engineering Methods
Advanced feature engineering techniques are crucial for transforming raw data into powerful predictive models, leading to deeper insights and improved outcomes. Deep feature synthesis involves creating new features by combining existing ones, such as feature crossing, to capture interactions.
Automated tools reduce manual effort and suggest meaningful transformations, while PCA and FeatureTools aid in dimensionality reduction and feature matrix generation.
Binning techniques, such as quantile binning, categorize continuous features, while clustering methods group similar data points to enhance feature sets; iterative testing is essential for refining these features.
Polynomial Features
Polynomial feature generation can identify nonlinear relationships by producing new features based on existing ones raised to a power or multiplied together. New features impact model predictions by making them more relevant to the problem.
For instance, creating polynomial features from variables like Length and Breadth can help capture nonlinear relationships that are not immediately apparent in the raw data, leading to improved model performance.
Time-Based Feature Extraction
Extracting features that capture temporal dynamics from time-series data can significantly improve predictions. New features based on previous time steps of a time series provide context that can enhance predictions. Statistics calculated over a rolling window of time capture trends and seasonal patterns, providing deeper insights into the temporal behavior of the data.
For example, calculating the average sales over the past week or month can help identify trends and inform future sales predictions.
Embedding Representations
Embedding representations in feature engineering maps categorical variables to continuous vector spaces to reduce dimensionality. Embedding representations maps high-cardinality categorical variables into continuous vector spaces, reducing dimensionality while retaining relationships. This technique improves model performance by capturing complex relationships and hierarchies among categories.
For example, using neural networks to create embeddings for categorical features like product IDs or user IDs can help capture underlying patterns and improve predictions.
Feature Engineering in Python
To illustrate the concepts discussed, we’ll provide a hands-on example of feature engineering using Python libraries like Pandas, Scikit-Learn, and Feature-Engine. Featuretools is a Python library for automatic feature engineering for structured data, while TsFresh is a Python package for calculating time series features.
A practical example of feature engineering is a house price prediction dataset with 81 columns. By using these libraries, data scientists can automate and streamline the feature engineering process, improving efficiency and model performance.
Using Pandas for Data Manipulation
Pandas is mainly utilized for managing structured data. It is a powerful tool for data manipulation and analysis. It makes data cleaning and manipulation easier, allowing data scientists to preprocess raw data effectively. Pandas allows data scientists to remove duplicates, handle missing values, and create new features, essential steps in feature engineering.
Applying Scikit-Learn for Feature Engineering
Scikit-Learn offers various methods for encoding categorical variables, such as one-hot encoding and label encoding, which convert categorical data into numerical formats suitable for machine learning algorithms. Feature scaling is crucial in Scikit-Learn as it ensures that features are on a similar scale, improving model effectiveness by preventing certain features from dominating others due to their magnitude.
Scikit-Learn provides several feature selection techniques, such as selecting features based on importance scores from models or using techniques like Recursive Feature Elimination (RFE) to optimize model performance.
Leveraging Feature-Engine Library
Feature-engine is an open-source Python library for feature engineering. It offers functionalities such as:
Transforms for missing data imputation
Outlier handling
Feature selection
Discretization
Feature-engine is fully compatible with Scikit-Learn, allowing for seamless integration in machine learning workflows.
Feature-engine automating feature engineering, many tasks, saving time, and improving overall engineering features efficiency with automated feature engineering tools. Additionally, we can automate feature engineering to enhance the process further.
Best Practices for Effective Feature Engineering
Selecting the right features impacts model accuracy and efficiency. Techniques to determine relevant features include exploratory data analysis, domain knowledge, and feature selection algorithms. Effective feature selection prevents overfitting, reduces computational complexity, and improves model performance.
Understanding the data context is essential for implementing advanced feature engineering techniques. Generating polynomial features can help model interactions between features for linear models.
Hands-on implementation is crucial for a thorough understanding of feature engineering techniques. Utilizing automated tools can greatly enhance efficiency in feature engineering by streamlining the process of automating data science endeavors through feature generation and selection.
Know Your Data
Data scientists dedicate around 80% of their time to feature engineering in data engineering. This process is crucial for building effective models. Understanding the dataset and its context is crucial for a data scientist. Domain knowledge and EDA help identify valuable insights from raw data.
By thoroughly understanding all the data points and their relationships, data scientists can create more meaningful and relevant features in data science.
Conduct Exploratory Data Analysis (EDA)
Conducting EDA helps identify patterns and relationships in data using visualizations and summary statistics. Exploratory Data Analysis should include visualizations to uncover underlying patterns in the data.
Performing EDA helps data scientists understand data distributions and relationships, guiding the feature engineering process and leading to more effective models.
Iterative Testing and Validation
Iterative testing is critical in feature engineering to systematically refine features based on model feedback. Improving model performance relies heavily on an iterative process that allows teams to test, validate, and adjust features.
Each iteration helps uncover feature effectiveness and highlights opportunities for improvement through model accuracy assessments. Cross-validation techniques assess feature sets' performance over multiple data splits, enabling better validation.
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Summary
In summary, feature engineering is a critical process in machine learning that transforms raw data into meaningful features, significantly improving model performance. By understanding and implementing various feature engineering techniques, data scientists can enhance model accuracy, interpretability, and efficiency.
Feature engineering covers a wide range of strategies, from filling in missing values and encoding categories to more advanced techniques like creating polynomial features or using embeddings. When done right, it can make a huge difference in how well your machine learning models perform. With the right best practices and tools (including automated ones), data scientists can speed up the process and get more accurate, actionable insights from their data. If you’re building a team that knows how to make data work smarter, Fonzi AI helps you find top AI and data talent with hands-on experience in feature engineering, so you can boost performance from day one.
