DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data

TL;DR

DiffPrep introduces a differentiable approach to automatically search for optimal data preprocessing pipelines for tabular data, significantly improving model accuracy with reduced computational cost.

Contribution

It formalizes data preprocessing pipeline search as a bi-level optimization problem and proposes a differentiable method that enables efficient gradient-based search.

Findings

Achieved best test accuracy on 15 out of 18 datasets

Improved test accuracy by up to 6.6 percentage points

Reduces the need for multiple model trainings during pipeline search

Abstract

Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models. However, it can be costly and time-consuming, often requiring the expertise of domain experts. Existing automated machine learning (AutoML) frameworks claim to automate data preprocessing. However, they often use a restricted search space of data preprocessing pipelines which limits the potential performance gains, and they are often too slow as they require training the ML model multiple times. In this paper, we propose DiffPrep, a method that can automatically and efficiently search for a data preprocessing pipeline for a given tabular dataset and a differentiable ML model such that the performance of the ML model is maximized. We formalize the problem of data preprocessing pipeline search as a bi-level optimization problem. To solve this problem efficiently, we transform and relax the discrete, non-differential search space into a continuous and differentiable one, which allows us to perform the pipeline search using gradient descent with training the ML model only once. Our experiments show that DiffPrep achieves the best test accuracy on 15 out of the 18 real-world datasets evaluated and improves the model's test accuracy by up to 6.6 percentage points.