Pitfalls Analyzer: Quality Control for Model-Driven Data Science Pipelines
Bibliographic record
Abstract
Data science pipelines are a sequence of data processing steps that aim to derive knowledge and insights from raw data. Data science pipeline tools simplify the creation and automation of data science pipelines by providing reusable building blocks that users can drag and drop into their pipelines. Such a graphical, model-driven approach enables users with limited data science expertise to create complex pipelines. However, recent studies show that there exist several data science pitfalls that can yield spurious results and, consequently, misleading insights. Yet, none of the popular pipeline tools have built-in quality control measures to detect these pitfalls. Therefore, in this paper, we propose an approach called Pitfalls Analyzer to detect common pitfalls in data science pipelines. As a proof-of-concept, we implemented a prototype of the Pitfalls Analyzer for KNIME, which is one of the most popular data science pipeline tools. Our prototype is model-driven, since the detection of pitfalls is accomplished using pipelines that were created with KNIME building blocks. To showcase the effectiveness of our approach, we run our prototype on 11 pipelines that were created by KNIME experts for 3 Internet-of-Things (IoT) projects. The results indicate that our prototype flags all and only those instances of the pitfalls that we were able to flag while manually inspecting the pipelines.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.080 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".