Your data is teeming with potential insights, ready to be teased out by predictive models. But doing that isn’t only about knowing what questions to ask or how to translate them into the right kinds of algorithms. It’s also about identifying and creating the most fruitful, predictive indicators — the features — in the dataset. This is the foundation of feature engineering.
Feature engineering involves ideating, selecting, and creating useful features in your datasets, helping you to identify potentially fruitful connections, correlations, and patterns to explore in your machine learning models.
Figuring out what features to create generally takes a fair bit of domain knowledge. You need to understand the business context to pinpoint which features are relevant and valuable, and to make an informed judgment on how these data points might interrelate in significant, predictive ways. You need to be able to make an informed assessment of whether apparent correlations between data might be meaningful and even causational or whether this is just a coincidence.
You also need to understand the data and the world it reflects well enough to know what it doesn’t tell you — and what external data you might have to bring in to fill out these gaps and engineer new features. Only after all this careful data analysis will you be able to select, create, and test the most useful new features in the data.
Doing this manually is a tricky and time-consuming task. You’re constrained both by the bandwidth of your team and by the limits of your imagination. Even the most creative data scientists will naturally gravitate to features and connections they’ve seen in previous datasets or that proved to be useful in other machine learning models. This is a great start but means you may miss unique feature options for this particular use case that you haven’t come across before, in your personal experience. You don’t know what you don’t know, after all.
What’s more, you will also need to quality check your choices by testing each individual feature and evaluating these new features against your existing machine learning models, identifying the ones that deliver the greatest improvements to your results. That might be fine when you’re only using a very small model, but if you’re looking to scale up your model or you’re working with huge datasets, that’s a very big job indeed. Without a swift and streamlined way to do this, this will lead to long lead times to deployment.
These setbacks have created a strong argument for automating as much of the feature engineering process as possible. But what is automated feature engineering?
This involves scanning through your dataset using AI-powered tools and automatically extracting the best features, or predictive variables, to answer the questions your machine learning model is trying to answer. Rather than painstakingly trawling through the dataset looking for features, this allows you to approach the problem fast and efficiently while also teasing out connections that might not be obvious to a human analyst.
Even better, the best-automated feature engineering tools won’t just suggest potential features, they will also assess, test, and rank thousands of options by relevance and usefulness. This means that you, the expert, can start with the features most likely to help you, removing any from the list that aren’t a great fit, and work your way down the list. Again, it’s a much more efficient way of doing things.
What is feature engineering without data? As we’ve seen, feature selection and creation isn’t just about extrapolating new columns from your existing dataset; it’s also about spotting the gaps in your dataset and figuring out what supplementary data would be most useful to fill out the full picture.
One of the great things about automated feature engineering tools is that the best of these combine feature generation with augmented data discovery. For example, you might upload your internal data to the augmented data discovery platform and automatically search through thousands of connected, compatible, external data sources to find relevant data points and columns. You can then add these to your dataset quickly and easily.
From there, the data science platform will then deploy its AI-backed data matching and automated feature engineering functions, compiling a list of thousands of potential features. Depending on the platform you use, this should allow you to interrogate all kinds of data sources to identify and generate useful features. For example, geospatial data, ZIP codes, footfall data, local property attributes, time series analysis (for seasonal trends and the impact of specific dates and holidays), plain text using natural language processing and text analysis, social media data, and search queries. It should also filter, test, and rank the results.
The system will use AI to optimize the features list, working out which features are most relevant to your dataset and which have the biggest impact on the accuracy of your model. It will then present around 50-100 features to you for consideration, rather than bombarding you with the thousands of potential ideas it initially came up with.
Feature engineering is essential for getting the most value out of your precious data, but no company has the time to do this all by hand. Plus, if you did, the chances are that by the time your model was ready to deploy, the data would already be a little out of date – and you’d have missed your moment.
Automated feature engineering streamlines the entire process, removing the barriers and helping you automate better features. It makes your data work harder and your features more effective than you could ever dream of when doing this by hand.