Can We Do Better with What We Have Done? Unveiling the Potential of ML Pipeline in Notebooks

Computational notebooks are widely adopted by data scientists for experimenting with machine learning (ML) models. Despite the support for exploratory programming enabled by notebooks, they fall short in the ability to manage alternatives across different stages of the ML pipeline. In this study, we conduct a qualitative analysis to examine how data scientists explore various alternatives through a series of versions of notebooks on Kaggle. The findings indicate that data scientists investigate multiple alternatives at each stage across multiple versions, yet only a limited number of combinations from different stages are explored. Next, by combining alternatives from all stages to form previously unexplored paths, we discover that certain untested combinations of alternatives can outperform the best models as identified in the original notebooks. Moreover, by substituting the hyperparameter optimization and model configuration stages with AutoML methods, we observe that only a select number of ML pipelines experience improvement via AutoML, which implies the limitation of the current AutoML techniques. In summary, our study provides insights into the systematic and effective exploration of overlooked ML pipeline configuration combinations that yield superior results. The findings shed light on future research directions such as the development of tooling support of alternative management while striking a balance between manual exploration and automated optimization.