The Importance of Feature Engineering in Machine Learning

In machine learning, where algorithms are designed to learn patterns and make predictions from data, there’s a hidden operative at work that plays a crucial role in training the machine learning models—feature engineering. Often overshadowed by sophisticated algorithms and powerful hardware, the process of feature engineering is an art that can significantly impact the success of a machine learning model.

In this blog post, we’ll delve into the importance of feature engineering, its role in enhancing model performance and the components that make up this essential piece of machine learning.

What Is Feature Engineering?

In engineering terms, a feature is a measurable input that can be used by predictive models. Simply put, features are variables.¹ Thus, feature engineering is the process of selecting, transforming and creating relevant input variables (features) from raw data for use in supervised learning.² Feature engineering bridges the raw data and the algorithm, shaping the data into a form that lends itself to effective learning. Its goal is to simplify and speed up data transformations, as well as enhance machine learning model accuracy.²

Why Is Feature Engineering Important?

The quality and relevance of features are crucial for empowering machine learning algorithms and obtaining valuable insights. Overall, feature engineering is used to:³

• Improve model performance: As previously noted, features are key to the optimal performance of machine learning models. Features can be thought of as the recipe and the output of the model can be thought of as the meal
• Lessen computational costs: When done right, feature engineering results in reduced computational requirements, like storage, and can improve the user experience by reducing latency
• Improve model interpretability: Speaking to a human’s ability to predict a machine learning model’s outcomes, well-chosen features can assist with interpretability by helping explain why a model is marking certain predictions

Feature engineering will ultimately determine if a predictive model succeeds or fails. A popular example of feature engineering is the Titanic Competition, which challenges users who are part of an online community to use feature engineering and machine learning models to predict which passengers will survive the Titanic’s sinking.¹

Component Processes of Feature Engineering

There are four processes that are essential to feature engineering—creation, transformation, extraction and selection.¹ Let’s explore each in more detail.

Feature Creation

You can think of this process in terms of an artist deciding which colors to use in a new painting. Engineers identify the most useful variables for the specific predictive model. They will look at existing features and then add, remove, multiply or ratio what currently exists to create new features for better predictions.¹

Transformations

Transformations modify the data to fit a certain range or statistical distribution. This is especially important with small data sets and simple models where there may not be enough capacity to learn the relevant patterns in the data otherwise.¹

Feature Extraction

In this process, new variables are automatically created through extraction from raw data. The aim here is to reduce the data volume you are working with in order to create features that are more manageable for the model.¹

Feature extraction methods include:¹

• Cluster analysis
• Text analytics
• Edge detection algorithms
• Principal components analysis

Feature Selection

At this stage, engineers pick which features are relevant and which should be removed, because they are either irrelevant or redundant.¹ This process is all about deciding which parts are not needed, which parts are repeating the same information and which parts are the most crucial for the model to be successful.

Examples of Feature Engineering Across Industries

Here are some examples of projects across different industries and domains where feature engineering plays a crucial role:

Credit Scoring

Creating features reflect a person’s credit history, such as the number of late payments, the ratio of credit used to credit available, the age of accounts and the diversity of credit types.

Fraud Detection

Engineers derive patterns from transaction data, such as the frequency of transactions, unusual amounts for a specific account, or the time elapsed between transactions to identify potential fraud.

Customer Segmentation

Purchase history and customer demographics can be combined to create customer segments for targeted marketing campaigns.

Predictive Maintenance

Sensor data from machines can be used to create features that predict when equipment might fail, such as the mean time between anomalies or the variance in temperature readings.

Real Estate Price Prediction

Various property attributes (like square footage, number of bedrooms and neighborhood crime rate) can be combined to predict housing prices more accurately.

Sentiment Analysis

Engineers can craft features from text data such as word counts, presence of specific keywords and sentiment scores to analyze overall customer sentiment.

Churn Prediction

Analysts can craft features to predict customer churn, such as the frequency of service use, duration since the last engagement and changes in usage patterns.

Health Risk Assessment

Features can be generated from patient records and historical data to predict health risks or readmission rates, like the number of previous hospitalizations or the combination of comorbidities.

Stock Market Prediction

Features can be crafted from market data, such as moving averages, price volatility or trading volumes, to forecast stock price movements.

Image Recognition

Algorithms can be used to extract key features from image data, such as edges, corners, and textures to improve object detection or facial recognition systems.

Natural Language Processing (NLP)

Engineers can extract features from text for various NLP tasks, such as topic detection, text classification or language translation.

In all these projects, feature engineering is used to transform raw data into informative features that can significantly enhance the effectiveness of machine learning models. The goal is to provide models with meaningful inputs that capture the underlying patterns in the data, leading to more accurate predictions or classifications.

Go Further With MSOE’s Machine Machine Learning Online Programs

As machine learning evolves, so will feature engineering. While automated feature selection and extraction methods are gaining traction, the human touch of domain knowledge and creativity remains irreplaceable.³ Mastering the art of feature engineering is an essential skill for professionals working in machine learning. Successful professionals will also appreciate and master the delicate balance between science and art in the world of data-driven insights.

Ready to grow your machine learning expertise? The curriculum for Milwaukee School of Engineering’s online Master of Science in Machine Learning offers in-depth courses that explore concepts like feature engineering that you need to be successful as a leader in machine learning. This program equips you with real-world experience and focuses on how to use machine learning technologies to solve complex industrial problems. Through this program, you will gain skills that can be applied immediately to your current work, boosting your value and leadership abilities well before graduation.

If you’re not quite ready for a master’s program, MSOE also offers an online Graduate Certificate in Applied Machine Learning. The program consists of two application-oriented courses that fuse concepts from statistics and computer science to design algorithms and software that process data, make predictions and aid decision-making. After completing your certificate, you can apply your earned credits to the full master’s program.

Don’t wait to take the next step in your career. Complete the form below for more information, or get started on your application.