PreCall: A Visual Interface for Threshold Optimization in ML Model Selection
Pith reviewed 2026-05-24 22:56 UTC · model grok-4.3
The pith
An interactive interface visualizes how adjusting a classification threshold changes precision, recall, and expected application results in a machine learning system.
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
PreCall closes the translation gap between application requirements and model parameters by interactively visualizing the relationship between major model metrics (recall, precision, false positive rate) and a parameter (the threshold between valuable and damaging edits) while also showing the probable results for the current model configuration.
What carries the argument
Interactive plots that map performance metrics to the decision threshold together with previews of likely outcomes under that threshold.
If this is right
- Domain experts can map real-world needs directly onto model settings without first learning formal parameter spaces.
- Trade-offs among recall, precision, and false-positive rate become visible as the threshold moves.
- Users can preview how a chosen setting would classify future items before deployment.
- The same visual linkage supports decisions at different levels of automation.
Where Pith is reading between the lines
- The same metric-to-outcome plots could be reused for threshold decisions in other content-filtering or moderation systems.
- Adding controls for additional parameters beyond the single threshold would test whether the visualization approach scales.
- Logging the thresholds users actually select during real tasks would show whether the interface changes behavior compared with numeric-only tools.
Load-bearing premise
Showing users interactive plots of metrics versus threshold and simulated results will improve their understanding and ability to choose appropriate settings.
What would settle it
A controlled study in which participants given the interface select thresholds no more accurately or consistently than participants given only numeric metric tables.
read the original abstract
Machine learning systems are ubiquitous in various kinds of digital applications and have a huge impact on our everyday life. But a lack of explainability and interpretability of such systems hinders meaningful participation by people, especially by those without a technical background. Interactive visual interfaces (e.g., providing means for manipulating parameters in the user interface) can help tackle this challenge. In this paper we present PreCall, an interactive visual interface for ORES, a machine learning-based web service for Wikimedia projects such as Wikipedia. While ORES can be used for a number of settings, it can be challenging to translate requirements from the application domain into formal parameter sets needed to configure the ORES models. Assisting Wikipedia editors in finding damaging edits, for example, can be realized at various stages of automatization, which might impact the precision of the applied model. Our prototype PreCall attempts to close this translation gap by interactively visualizing the relationship between major model metrics (recall, precision, false positive rate) and a parameter (the threshold between valuable and damaging edits). Furthermore, PreCall visualizes the probable results for the current model configuration to improve the human's understanding of the relationship between metrics and outcome when using ORES. We describe PreCall's components and present a use case that highlights the benefits of our approach. Finally, we pose further research questions we would like to discuss during the workshop.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The manuscript presents PreCall, an interactive visual interface for the ORES ML service in Wikimedia projects. It visualizes the effects of varying the decision threshold on metrics including recall, precision, and false positive rate, displays probable outcomes for a given configuration, describes the interface components, provides a use case, and poses open research questions for workshop discussion. The work aims to help non-technical users translate domain requirements into model parameters.
Significance. If the visualizations succeed in improving user understanding of metric-threshold relationships, the design could meaningfully support participatory configuration of ML systems in high-impact settings such as Wikipedia edit quality assessment. The prototype contributes a concrete example of interactive visualization for threshold tuning, which is relevant to the broader area of human-in-the-loop ML and explainable interfaces.
major comments (2)
- [Abstract] Abstract: The text states that PreCall is intended 'to improve the human's understanding of the relationship between metrics and outcome when using ORES', yet the only supporting material is a descriptive use case. No user study, A/B comparison, task-performance metrics, or qualitative feedback is reported to substantiate any improvement in understanding or configuration quality.
- [Use case] Use-case description: The narrative highlights benefits of the interface but supplies no baseline (e.g., existing ORES configuration tools), no measurement of user accuracy or time, and no falsifiable prediction about when the visualizations would or would not help. This leaves the central claim about closing the 'translation gap' without empirical grounding.
Simulated Author's Rebuttal
We thank the referee for the constructive comments. This is a workshop paper presenting a prototype interface and illustrative use case to stimulate discussion on human-in-the-loop ML configuration, rather than reporting a completed empirical evaluation. We address the points below and will make revisions to clarify scope and intent.
read point-by-point responses
-
Referee: [Abstract] Abstract: The text states that PreCall is intended 'to improve the human's understanding of the relationship between metrics and outcome when using ORES', yet the only supporting material is a descriptive use case. No user study, A/B comparison, task-performance metrics, or qualitative feedback is reported to substantiate any improvement in understanding or configuration quality.
Authors: We agree that the manuscript provides no empirical evidence of improved understanding. The abstract phrasing describes the intended design goal of the prototype rather than a demonstrated result. As a workshop submission, the focus is on the interface design, components, and open questions rather than evaluation. We will revise the abstract to explicitly state that PreCall is a prototype intended to support understanding of metric-threshold relationships, that the use case is illustrative, and that empirical validation remains future work. revision: yes
-
Referee: [Use case] Use-case description: The narrative highlights benefits of the interface but supplies no baseline (e.g., existing ORES configuration tools), no measurement of user accuracy or time, and no falsifiable prediction about when the visualizations would or would not help. This leaves the central claim about closing the 'translation gap' without empirical grounding.
Authors: The use case is presented as a narrative scenario to demonstrate how the visualizations could be applied in Wikipedia edit quality assessment and to surface research questions for workshop discussion. It does not constitute an empirical claim or evaluation. We acknowledge the lack of baselines, measurements, or falsifiable predictions. In revision we will add explicit language noting that the use case is hypothetical and exploratory, and that the paper does not claim validated improvements in closing the translation gap. revision: yes
Circularity Check
No significant circularity
full rationale
The paper is a descriptive prototype design contribution with no derivation chain, equations, predictions, or fitted parameters. It presents an interface for visualizing ORES metrics vs. threshold and poses open research questions. No load-bearing steps exist that could reduce to self-definition, fitted inputs, or self-citations. The contribution is explicitly framed as design description rather than any asserted result derived from inputs.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.