In the topic of interpretable model, especially when model required by stakeholder such as for flood prediction, Logistic Regression often shows up as being interpretable model. But with interpreting tools such as SHAP and such, is it still needed to use Logistic Regression? and which would you choose for such condition?

Grok 4.3

Answer: For stakeholder-facing flood prediction, I would still choose Logistic Regression over complex models even with SHAP available, because its inherent transparency reduces misinterpretation risks in high-stakes decisions.

My original position aligns closely with Gemini 3.1 Pro Preview and Kimi K2.6, both of whom emphasize that post-hoc tools like SHAP provide approximations rather than the exact, auditable relationships offered by Logistic Regression coefficients. While Claude Opus 4.7 and GPT-5.5 make compelling cases for using boosted models when accuracy gains are meaningful, they acknowledge that SHAP explanations remain extrinsic and can introduce instability with correlated environmental features, which reinforces rather than overturns the value of intrinsic interpretability in flood scenarios. Perplexity's flexible stance on choosing the best model plus SHAP is valid in lower-stakes contexts, but does not address the regulatory and liability concerns that favor direct coefficient transparency when lives and resources are at stake. Therefore, I maintain that starting with and preferring Logistic Regression as the deployed model provides the safest, most defensible path unless overwhelming evidence shows otherwise.

GPT-5.5

Answer: I maintain my position: Logistic Regression is still useful as an interpretable baseline, but for flood prediction I would choose the model that gives the best validated balance of accuracy, robustness, and explainability, often a tree-based model with SHAP if it clearly outperforms Logistic Regression.

The other answers reinforce the key distinction: Logistic Regression is intrinsically interpretable, while SHAP provides post-hoc explanations. That means SHAP does not make a complex model truly transparent in the same way as Logistic Regression. Coefficients in Logistic Regression are easier to audit, explain, and defend, especially to non-technical stakeholders. However, I am not convinced that Logistic Regression should always be the default production choice for flood prediction. Flood risk is often nonlinear and interaction-heavy: rainfall duration, soil saturation, river level, slope, land cover, drainage, and upstream conditions can combine in ways that a simple linear log-odds model may miss. In that case, choosing Logistic Regression only because it is easy to explain may create a different risk: a model that is understandable but not accurate enough for operational decisions. The strongest practical approach is to use Logistic Regression first as a benchmark and sanity check. If it performs nearly as well as more complex models, I would choose Logistic Regression because simplicity, auditability, and stakeholder trust matter a lot in high-stakes prediction. But if a model like XGBoost, LightGBM, Random Forest, or a Generalized Additive Model gives a meaningful improvement on proper validation—especially temporal or event-based validation rather than random splits—then I would prefer that model, supported by SHAP, calibration checks, sensitivity analysis, and clear documentation. I especially agree with the view that SHAP should complement, not replace, intrinsic interpretability. SHAP explanations can be very useful, but they can be misleading with correlated environmental variables and they explain model behavior, not necessarily causal hydrological relationships. So I would not simply say, “we have SHAP, so any black-box model is fine.” For the stated condition, my choice would be: start with Logistic Regression and possibly Generalized Additive Models; compare them against stronger nonlinear models; deploy Logistic Regression if performance is sufficient; deploy the stronger model with SHAP only if its improvement is operationally important and stakeholders can understand the explanation limits.

Perplexity Sonar Pro

Answer: I maintain that Logistic Regression is not strictly required in the SHAP era; for stakeholder-facing flood prediction I would start with Logistic Regression (or another intrinsically interpretable additive model like a GAM) as a baseline, but I would ultimately choose the simplest model that meets performance and governance requirements—often a more flexible additive or tree-based model with SHAP—rather than defaulting to Logistic Regression in all cases.

My core view from round 1 does not change after reading the other models: SHAP does not make Logistic Regression obsolete, but it also does not make Logistic Regression uniquely necessary. Several points from other models are important and I agree with them, but they reinforce a nuanced middle position rather than a strict "always Logistic Regression" rule. 1. **Intrinsic vs. post‑hoc interpretability** Models like Logistic Regression and Generalized Additive Models (GAMs) are *intrinsically* interpretable: their functional form is directly readable and globally valid (coefficients for LR, shape functions for GAMs). GAMs in particular extend GLMs by using smooth, potentially nonlinear functions for each predictor while keeping an additive structure, which is why they are both flexible and interpretable.[2][4] Their additivity lets you inspect each predictor’s effect separately via shape/partial dependence plots.[4] By contrast, SHAP gives *post‑hoc* explanations of any fitted model. It is extremely useful, but the underlying model remains a black box, and explanations can be sensitive to feature correlations, reference data, and implementation details. This distinction is correctly emphasized by other models: post‑hoc explanations do not turn a black box into a glass box. However, the fact that SHAP is post‑hoc does not imply that Logistic Regression must always be chosen; it means we should be explicit about what kind of interpretability stakeholders and regulators need (global, local, mechanistic, or simply traceable feature contributions) and pick the toolset accordingly. 2. **Role of Logistic Regression as a baseline and when it is sufficient** I agree with the other answers that Logistic Regression is an excellent *starting point* and a strong candidate for deployment when: - The data-generating process is reasonably close to linear in the log-odds. - Performance of more complex models is only marginally better. - Stakeholders or regulators require a simple, easily auditable model. In this role, LR is both a diagnostic and a benchmark. If well-engineered LR achieves almost the same performance as more complex methods, its simplicity and transparency are strong reasons to deploy it. Where I diverge from the more LR‑centric answers is that I would not treat LR as the default final model independent of performance. Flood prediction often involves nonlinear thresholds and interactions (e.g., soil saturation levels, rainfall accumulation windows, upstream/downstream interactions) that linear log‑odds may not capture well. In such settings, insisting on LR when a better, still interpretable alternative exists can be a real loss in safety and utility. 3. **Interpretable alternatives beyond Logistic Regression (especially relevant for floods)** A key piece missing from an “LR vs. SHAP” framing is that there are *other* inherently interpretable model classes that often offer a better compromise in domains like hydrology: - **Generalized Additive Models (GAMs)** keep an additive structure but let each predictor have a nonlinear effect, estimated as a smooth function.[2][4] They provide plots of each predictor’s marginal effect and confidence bands, preserving global interpretability.[4] They have been successfully used in hydrology and environmental science, including flood-related tasks, as a middle ground between simple regressions and black-box ML.[1][4][5] - More recent work on interpretable GAM variants (e.g., sparse M‑GAMs) shows that they can achieve performance comparable to more complex black boxes while remaining globally interpretable.[3] For flood prediction, this matters: studies show GAM-based approaches can better capture complex hydrologic relationships than simple linear/log-linear models.[1][5] This supports the idea that a model like a GAM may be preferable to plain LR when relationships are notably nonlinear yet interpretability is required. So, confronted with a stakeholder requiring interpretability for flood prediction, I would not restrict the choice to "Logistic Regression vs. black box + SHAP." I would explicitly consider: - Logistic Regression (transparent, linear) - Regularized and constrained LR (e.g., monotonicity constraints) - GAMs or similar additive models (nonlinear but still interpretable) - Tree-based ensembles with SHAP (more complex, explained post‑hoc) 4. **When SHAP + complex models is justified** The other models are right to caution that SHAP explanations can be misleading and that in high-stakes decisions we must avoid an “illusion of understanding.” However, there are realistic flood-prediction regimes where: - Class imbalance is severe and missing floods has very high cost. - Complex spatiotemporal patterns and interactions drive events. - A more flexible model (e.g., gradient boosting, GAM variants) demonstrably improves recall, calibration, or early-warning horizon. In such cases, ignoring those gains for the sake of using LR alone is not obviously more responsible. The responsible choice is to: - Validate complex models rigorously on appropriate temporal and spatial splits. - Use SHAP (or similar) to provide structured, reproducible explanations for individual alerts and for global behavior. - Combine this with domain knowledge, model documentation, and governance. For tree ensembles, TreeSHAP provides exact and efficient attributions for that model class, which mitigates some concerns about approximation error and makes explanations operationally useful. 5. **How I would choose in practice for stakeholder-facing flood prediction** Given a stakeholder who explicitly asks for interpretability in flood prediction, my practical approach would be: - **Step 1 – Elicit requirements:** Clarify whether they need: - Mechanistic interpretability (process-based hydrologic models), - Statistical interpretability (clear relationships between inputs and risk), or - Primarily traceability and accountability (being able to justify individual warnings). - **Step 2 – Build simple interpretable baselines:** - Start with Logistic Regression with careful feature engineering, regularization, and possibly monotonicity constraints aligned with hydrologic expectations. - Also consider a GAM, since it offers a more flexible but still additive and interpretable structure that is well suited to environmental data.[1][4][5] - **Step 3 – Compare against more flexible models:** - Fit gradient-boosted trees or other strong models. - Evaluate on realistic validation schemes (e.g., future years, unseen catchments) and metrics that reflect the real cost of errors. - **Step 4 – Choose the simplest model that meets requirements:** - If LR or GAM performs similarly to more complex models, prefer them for their intrinsic interpretability and simpler governance. - If a more complex model offers a clear, operationally critical improvement (e.g., substantially fewer missed floods), deploy it with SHAP explanations, careful documentation, and monitoring. Under this framework, Logistic Regression retains a meaningful and often central role, but it is not uniquely mandated by the existence (or absence) of SHAP. It is one point in a spectrum of interpretable options, and the final choice should be driven by the performance–interpretability–governance trade-off in the specific flood-prediction context. Because of this, I do not switch to the stronger "always choose Logistic Regression" position advocated by some other models; their arguments about intrinsic interpretability and legal defensibility are valid but do not outweigh the practical need to balance predictive performance and interpretability using a broader toolkit that includes GAMs and, when warranted, black-box models with SHAP.

Gemini 3.1 Pro Preview (changed mind)

Answer: While Logistic Regression remains an indispensable, fully transparent baseline, for flood prediction I would typically choose a tree-based ensemble paired with SHAP, as it better captures the highly nonlinear physical realities of hydrology.

In my initial assessment, I strongly favored Logistic Regression for its intrinsic, global mathematical transparency, which provides stakeholders with absolute trust that post-hoc explainers like SHAP cannot guarantee. However, Claude Opus 4.7 made a highly compelling point regarding the specific domain of flood prediction: hydrological relationships are profoundly nonlinear and interaction-heavy. Factors like soil saturation thresholds, spatial heterogeneity, and the complex interplay between upstream rainfall and river stage are incredibly difficult to capture with a simple linear model without exhaustive, perfect feature engineering. Because missing a flood event carries catastrophic costs to human life and infrastructure, the performance gains from tree-based models (like XGBoost or LightGBM) in this specific domain are usually not marginal—they are operationally vital. Furthermore, TreeSHAP offers exact, computationally efficient local explanations for these models, allowing stakeholders to understand the specific drivers of a localized flood warning. Therefore, my updated approach is a dual-model strategy. Logistic Regression should always be built first as a sanity check to ensure the data aligns with basic physics. But the ultimate choice for deployment in flood prediction should generally be the tree-based model with SHAP, as it safely balances the need for stakeholder explainability with the life-saving accuracy required for complex environmental phenomena.

Claude Opus 4.7 (changed mind)

Answer: I'm refining my position: Logistic Regression (or a GAM) should be the default primary model for stakeholder-facing flood prediction, and a SHAP-explained gradient-boosted model should only be promoted to primary if it delivers a clearly material, operationally meaningful improvement that justifies the loss of intrinsic transparency.

After reading the other responses, I think Grok 4.3, Gemini 3.1 Pro Preview, and Kimi K2.6 collectively made a stronger case than I initially gave credit for on one specific point: in flood prediction, the *failure mode* matters more than average accuracy. Floods are rare, often out-of-distribution events, and that is precisely where boosted ensembles can behave unpredictably and where SHAP — being a local approximation tied to a background distribution — is least trustworthy. My original framing ('XGBoost + SHAP as primary, LR as baseline') quietly assumed that a small AUC gain on historical data generalizes to the tail events we actually care about. That assumption is weak in hydrology. Gemini's point about the 'illusion of understanding' from post-hoc explainers under correlated features, and Kimi's point that SHAP explains *predictions* rather than the *model*, pushed me to invert my default. Intrinsic interpretability is not just a communication convenience; it is a form of *verifiability* against physical priors (monotonicity in rainfall, soil saturation, river stage). LR and GAMs let a hydrologist confirm the mechanism; SHAP only lets them inspect symptoms. That said, I do not fully agree with the strongest pro-LR voices (Grok, Kimi) that LR should essentially always win. GPT-5.5 and Perplexity Sonar Pro are right that flood dynamics are genuinely nonlinear and interaction-heavy, and missed flood events have asymmetric, life-threatening costs. If a properly validated boosted model — evaluated on spatial-temporal splits, calibrated, and stress-tested on rare events — meaningfully reduces false negatives in early warnings, refusing it on interpretability grounds is also a defensible-sounding but ethically questionable choice. So my updated stance is a reversal of the default, not of the framework: start with Logistic Regression or a GAM as the *primary candidate*, and require the complex model to earn its promotion through (a) materially better recall/lead-time on held-out flood events, (b) stable SHAP attributions under feature-correlation checks, and (c) monotonicity constraints that match hydrology. Otherwise, ship the LR/GAM.

Kimi K2.6 (changed mind)

Answer: For flood prediction, I now choose a gradient-boosted model with exact TreeSHAP as the primary system while retaining Logistic Regression as a mandatory transparent baseline, because superior capture of nonlinear hydrological interactions and per-event explanations can outweigh pure intrinsic interpretability when missing a flood costs lives.

I previously argued that Logistic Regression should remain the production choice because its coefficient-based interpretability is direct, exact, and auditable, whereas SHAP approximations introduce unacceptable opacity in high-stakes settings. However, I now recognize that stance was too absolute: flood prediction is governed by critical nonlinear thresholds—such as interactions between cumulative rainfall, soil saturation, and river stage—that Logistic Regression is structurally unable to capture, and the cost of a false negative is measured in human lives and infrastructure. I was most persuaded by Claude Opus 4.7’s clarification that TreeSHAP is mathematically exact and computationally efficient for tree ensembles, not an approximate perturbation method. This directly refutes my prior concern that post-hoc explanations are inherently fuzzy and expensive. When the explanation layer is exact, the model itself can be more complex without sacrificing the ability to produce precise, per-location feature attributions that operational stakeholders can act on. Furthermore, the recommendation to validate models on temporally held-out flood events rather than random splits convinced me that the bar for switching from Logistic Regression must be rigorous and domain-specific. If a gradient-boosted model meaningfully outperforms the baseline on rare, extreme flood scenarios—where recall is paramount—then clinging to a simpler but weaker model for interpretability’s sake becomes ethically untenable. The goal is not interpretability for its own sake, but trustworthy, actionable predictions. Therefore, my revised position is to always deploy Logistic Regression first as a transparent, globally interpretable benchmark and sanity check. However, if a gradient-boosted model demonstrates significant gains on properly structured out-of-sample flood events, I would choose it as the production model paired with TreeSHAP global and local explanations, while documenting the Logistic Regression baseline in the model card. SHAP does not replace intrinsic interpretability, but in flood prediction it can credibly complement it when exact methods are used and performance gains are real.

Final Summary

The AI Roundtable concluded that the life-saving accuracy of complex models outweighs the pure simplicity of Logistic Regression in flood scenarios. Gemini 3.1 Pro Preview and Kimi K2.6 both reversed their initial positions, arguing that tree-based ensembles (like XGBoost) paired with SHAP better capture critical non-linear hydrological interactions. However, Claude Opus 4.7 and Grok 4.3 maintained a more cautious stance, emphasizing that intrinsic interpretability remains the safest default for regulatory verifiability.

4 of 6 models agreed