A growing number of businesses are looking to leverage machine learning (ML)—and they want to do it in the most effective and efficient ways possible. Automated feature engineering gives them the opportunity to achieve these goals.
Feature engineering—the process of using domain knowledge to extract new and crucial variables from raw data before applying ML—is a vital part of any data science project. Companies that want to make accurate predictions about events need to be able to separate the valuable data from the noise, and they want their data scientists to deploy models that can lead to tangible gains for the organization.
Automating feature engineering can potentially save companies time and money, as well as create better predictive models and useful features, and protect against data leakage.
The traditional, manual approach to feature engineering has been to create features one at a time using domain knowledge. But this is a tedious, time-consuming, and error-prone process. The code for manual feature engineering is problem-dependent and needs to be re-written for each new data set.
Automated feature engineering transforms the process by automatically extracting useful and meaningful features from a set of related data tables, using a framework that can be applied to any problem.
The key benefit of this new way of providing feature engineering is, naturally, automation. Enterprises are looking to automate anything they can, as they aim to speed up processes, improve accuracy, and reduce labor costs. Until recently, automation was not a part of the feature engineering aspect of machine learning. Now that it is, automated feature engineering is enhancing the way organizations leverage ML.
Better business decisions in financial services
For example, for a financial services application such as predicting whether a client will repay a loan, automated feature engineering can slash ML development time by as much as 10 times compared with manual feature engineering. At the same time it can provide better modeling performance.
A problem such as a loan repayment prediction might involve millions of rows of data spread across multiple tables. ML requires a single table for training, so feature engineering means consolidating all the data about each client in a single table.
With traditional manual feature engineering it could take at least 10 hours to create a set of features. With an automated feature engineering solution, the same process might take as little as one hour. It’s much faster because it requires less domain knowledge and there are far fewer lines of code to write.
To learn more about the business benefits of automated feature engineering, contact us.