Cognito: Automated Feature Engineering for Supervised Learning

TLDR

Feature engineering constructs novel features to improve predictive performance, yet it remains a human‑intensive, time‑consuming step central to data science. The paper introduces Cognito, a system that automatically performs feature engineering for supervised learning and demonstrates its efficacy on eight real datasets. Cognito explores feature construction choices hierarchically and non‑exhaustively, greedily maximizing model accuracy while allowing domain‑specific prioritization, sampling, parallelism, and integration with state‑of‑the‑art model selection. Experimental results on eight real datasets demonstrate Cognito’s efficacy in improving supervised learning performance.

Abstract

Feature engineering involves constructing novel features from given data with the goal of improving predictive learning performance. Feature engineering is predominantly a human-intensive and time consuming step that is central to the data science workflow. In this paper, we present a novel system called "Cognito", that performs automatic feature engineering on a given dataset for supervised learning. The system explores various feature construction choices in a hierarchical and non-exhaustive manner, while progressively maximizing the accuracy of the model through a greedy exploration strategy. Additionally, the system allows users to specify domain or data specific choices to prioritize the exploration. Cognito is capable of handling large datasets through sampling and built-in parallelism, and integrates well with a state-of-the-art model selection strategy. We present the design and operation of Cognito, along with experimental results on eight real datasets to demonstrate its efficacy.