Many quantitative science fields are adopting the paradigm of predictive modeling using machine learning. We welcome this development. At the same time, as researchers whose interests include the strengths and limits of machine learning, we have concerns about reproducibility and overoptimism.
There are many reasons for caution:
We focus on reproducibility issues in ML-based science, which involves making a scientific claim using the performance of the ML model as evidence. There is a much better known reproducibility crisis in research that uses traditional statistical methods. We also situate our work in contrast to other ML domains, such as methods research (creating and improving widely-applicable ML methods), ethics research (studying the ethical implications of ML methods), engineering applications (building or improving a product or service), and modeling contests (improving predictive performance on a fixed dataset created by an independent third party). Investigating the validity of claims in all of these areas is important, and there is ongoing work to address reproducibility issues in these domains.
The running list below consists of papers that highlight reproducibility failures or pitfalls in ML-based science. We find 20 papers from 17 fields where errors have been found, collectively affecting 329 papers and in some cases leading to wildly overoptimistic conclusions. In each case, data leakage causes errors in the modeling process.
|Field||Paper||Year||Num. papers reviewed||Num. papers w/pitfalls||Pitfalls|
|Medicine||Bouwmeester et al.||2012||71||27||No train-test split|
|Neuroimaging||Whelan et al.||2014||—||14||No train-test split; Feature selection on train and test set|
|Autism Diagnostics||Bone et al.||2015||—||3||Duplicates across train-test split; Sampling bias|
|Bioinformatics||Blagus et al.||2015||—||6||Pre-processing on train and test sets together|
|Nutrition research||Ivanescu et al.||2016||—||4||No train-test split|
|Software engineering||Tu et al.||2018||58||11||Temporal leakage|
|Toxicology||Alves et al.||2019||—||1||Duplicates across train-test split|
|Satelitte imaging||Nalepa et al.||2019||17||17||Non-independence between train and test sets|
|Clinical epidemiology||Christodoulou et al.||2019||71||48||Feature selection on train and test set|
|Tractography||Poulin et al.||2019||4||2||No train-test split|
|Brain-computer interfaces||Nakanishi et al.||2020||—||1||No train-test split|
|Histopathology||Oner et al.||2020||—||1||Non independence between train and test sets|
|Computer security||Arp et al.||2020||30||30||No train-test split; Pre-processing on train and test sets together; Illegitimate features; others|
|Neuropsychiatry||Poldrack et al.||2020||100||53||No train-test split; pre-processing on train and test sets together|
|Medicine||Vandewiele et al.||2021||24||21||Feature selection on train-test sets; Non-independence between train and test sets; Sampling bias|
|Radiology||Roberts et al.||2021||62||62||No train-test split; duplicates in train and test sets; sampling bias|
|IT Operations||Lyu et al.||2021||9||3||Temporal leakage|
|Medicine||Filho et al.||2021||—||1||Illegitimate features|
|Neuropsychiatry||Shim et al.||2021||—||1||Feature selection on training and test sets|
|Genomics||Barnett et al.||2022||41||23||Feature selection on training and test sets|
Data leakage has long been recognized as a leading cause of errors in ML applications. In formative work on leakage, Kaufman et al. provide an overview of different types of errors and give several recommendations for mitigating these errors. Since this paper was published, the ML community has investigated the impact of leakage in several engineering applications and modeling competitions. However, leakage occurring in ML-based science has not been comprehensively investigated. As a result, mitigations for data leakage in scientific applications of ML remain understudied.
Our taxonomy of data leakage highlights several failure modes which are prevalent in ML-based science. To address leakage, researchers using ML methods need to connect the performance of their ML models to their scientific claims. To detect cases of leakage, we provide a template for a model info sheet which should be included when making a scientific claim using predictive modeling. The template consists of precise arguments needed to justify the absence of leakage, and is inspired by Mitchell et al.'s model cards for increasing the transparency of ML models.
Model info sheets can be voluntarily used by researchers to detect leakage, or journals could encourage or require authors to provide them. Of course, model info sheets can’t prevent researchers from making false claims, but we hope they can make errors more apparent. Note that for model info sheets to be verified, the analysis must be computationally reproducible. Also, model info sheets don’t address reproducibility issues other than leakage. The model info sheet template is currently a beta version. We welcome feedback and are continuing to make changes to the template based on feedback.
We find that prominent studies on civil war prediction claiming superior performance of ML models over baseline Logistic Regression models fail to reproduce. Our results provide two reasons to be skeptical of the use of ML methods in this research area, by both questioning their usefulness and highlighting the pitfalls of applying them correctly. While none of these errors could have been caught by reading the papers, our model info sheets enable the detection of leakage in each case.Reproduction materials on CodeOcean List of papers in our systematic review
We acknowledge that there isn't consensus about the term reproducibility, and there have been a number of recent attempts to define the term and create consensus. One possible definition is computational reproducibility — when the results in a paper can be replicated using the exact code and dataset provided by the authors. We argue that this definition is too narrow because even cases of outright bugs in the code would not be considered irreproducible under this definition. Therefore we advocate for a standard where bugs and other errors in data analysis that change or challenge a paper's findings constitute irreproducibility. We elaborate this perspective here.
Reproducibility failures don’t mean a claim is wrong, just that evidence presented falls short of the accepted standard or that the claim only holds in a narrower set of circumstances than asserted. We don’t view reproducibility failures as signs that individual authors or teams are careless, and we don’t think any researcher is immune. One of us (Narayanan) has had multiple such failures in his applied-ML work and expects that it will probably happen again.
We call it a crisis for two related reasons. First, reproducibility failures in ML-based science are systemic. In nearly every scientific field that has carried out a systematic study of reproducibility issues, papers are plagued by common pitfalls. In many systematic reviews, a majority of the papers reviewed suffer from these pitfalls. Second, despite the urgency of addressing reproducibility failures, there aren’t yet any systemic solutions.