Ever wondered how exactly data scientists turn their data into models that are more effective and targeted for specific results? It’s feature engineering: the process of selecting, transforming, and creating new features from raw data to improve the performance of a machine learning model.
In this article, we’ll dive into the details of feature engineering, helping you understand how exactly it's an essential part of machine learning and break down the process so that you’re able to apply feature engineering principles to your own work.
Source: EliteDataScience
Why is Feature Engineering Important?
To put it simply, feature engineering is important because it improves the quality and performance of machine learning models. But to dive deeper, let’s discuss some of the other qualities that make feature engineering so crucial:
It leads to better models: with feature engineering, machine learning models are simply better; they have superior performance and are more reliable.
It leads to a better understanding of ML models: with good features, models are easier to understand and therefore work with.
It leads to a better understanding of your data: new features can provide you with previously unseen data points that could transform the way you work.
It leads to better data representation: with feature engineering, you can ensure that all data points are represented in your results.
It leads to reduced costs: prioritizing feature engineering could mean your costs are lower, such as storage.
It improves model efficiency: with feature engineering, models can be trained faster and more efficiently by focusing only on the most relevant data.
It enhances model generalization: with well-engineered features, models perform better on unseen data, leading to more accurate predictions in real-world scenarios.
Examples of Feature Engineering
Still not sure exactly what feature engineering is? Or how it appears in the real world? Luckily for you, we’ve outlined a few of feature engineering’s uses across various industries so that you’re able to better understand how to apply it to your own work:
Fraud detection: in order to flag potentially fraudulent transactions, feature engineering combines data from the following areas: transaction frequency, unusually high spending, transaction location, or time of transaction.
Credit scoring: feature engineering is able to determine a user’s credit score through comparing their credit history, number of late payments, ratio of credit used to credit available, and diversity of types of credits.
Health risks: to determine a patient’s likelihood for developing a disease or illness, or their probability of recovery, feature engineering allows healthcare providers the ability to compare historical data from other patients, along with their patient’s current results, to make the best possible estimate.
Image detection: ever wondered how exactly tools detect what’s in an image? Through feature engineering, of course, specific elements of an image are detected, such as edges, corners, and textures.
The Steps of Feature Engineering
Now that you understand two crucial parts of feature engineering (the importance of it and some real-life examples), let’s dive into the steps that you can actually use to transform raw data into predictive models: feature creation, transformations, feature extraction, and feature selection.
Feature creation
This initial step requires a fair amount of human input and brainstorming; after all, this is the stage where data scientists have to decide which variables will be the most useful in the predictive model and set up the model for success.
This means that now’s the time to ensure that your data is clean, useful, and easily accessible. The last thing you want is to dive deep into feature engineering and realize you don’t have access to the data you need to make a quality model.
Transformations
With your ideal features imagined and your data decided upon, it’s time to enter the transformation stage. Here, you’ll want to adjust the input data to ensure it works as well as possible. Here are a few examples of what this could look like:
Ensuring all features are measured the same, such as in dollars or percentages, makes sure it is balanced.
Creating clear connections between data points, if the data isn’t linear, can help the model perform better.
Using log transformation can ensure that skewed distributions are not improperly weighted within the model’s analysis.
Feature extraction
To further simplify the data analysis process, feature extraction automatically creates new and useful features from the raw data, which therefore speeds up and improves the modeling process. Cluster analysis is commonly used within feature extraction, which groups similar points together to simplify the dataset.
Feature selection
Once the model is ready to go, this last stage evaluates all the different features and how well they perform, highlighting those that perform better than others or those that are redundant. With this stage, you’ll also be able to see which are the most effective for your original purpose, helping you optimize the model and get rid of any unnecessary features.
Feature engineering is a game-changer for anyone working with machine learning. By selecting, transforming, and creating new features, you’re not only optimizing your models but also gaining a deeper understanding of your data. Whether it’s improving fraud detection, refining credit scores, or enhancing healthcare predictions, mastering feature engineering can elevate your work to the next level. Ready to see how it can transform your projects? Dive into the steps, apply them, and watch your models reach their full potential.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
