Deep Feature Synthesis: How Automated Feature Engineering Works

The artificial intelligence market is fueled by the potential to use data to change the world. While many organizations have already successfully adapted to this paradigm, applying machine learning to new problems is still challenging. The single biggest technical hurdle that machine learning algorithms must overcome is their need for processed data in order to work -- they can only make predictions from numeric data. This data is composed of relevant variables, known as "features." If the calculated features don't clearly expose the predictive signals, no amount of tuning can take a model to the next level.