---
name: statistical-model-selection-guide
description: "Recommends the most appropriate statistical or machine learning modeling approaches for a described prediction problem — matching regression, survival, tree-based, and probabilistic methods to the target type, dataset characteristics, interpretability constraints, and regulatory context. Delivers a ranked shortlist of 3-5 candidate models with rationale, assumptions, validation strategy, and a fallback if the primary approach fails. Use when deciding which model to build for a prediction or risk problem, comparing modeling approaches, or justifying a method choice to stakeholders or regulators."
argument-hint: "[prediction-problem] [dataset-profile]"
allowed-tools: ["Read"]
---

# Statistical Model Selection Guide

## Purpose
Translate a described prediction problem and dataset profile into a ranked shortlist of appropriate modeling approaches — with the assumptions, interpretability trade-offs, validation strategy, and regulatory fit spelled out — so the analyst starts building the right model instead of defaulting to whatever is familiar or reaching for a black box that won't pass review.

## When to Use
Invoke this skill when the user asks to:
- Decide which model to use for a specific prediction, classification, or risk problem
- Compare modeling approaches (e.g., GLM vs. gradient boosting, Cox vs. accelerated failure time)
- Choose a model that satisfies interpretability or regulatory constraints
- Recommend a survival, frequency-severity, or time-to-event modeling approach
- Justify a modeling method choice to a review board, actuary, or regulator
- Diagnose why a chosen model class isn't fitting and what to switch to

## Inputs Required
Before starting, collect:
- **Prediction target**: What is being predicted, and its type — continuous, binary, count, time-to-event, multiclass, ordinal, or probability
- **Business/risk objective**: The decision the model informs (e.g., pricing, reserving, fraud flagging, churn, claim propensity)
- **Dataset profile**: Row count, feature count, feature types (numeric/categorical), sparsity, and whether observations are independent or grouped/panel
- **Data quality notes**: Missingness pattern, class imbalance, censoring/truncation, outliers, known measurement issues
- **Interpretability requirement**: Fully transparent (coefficient-level explanation required) / partially interpretable (feature-level explanation acceptable) / black box acceptable
- **Regulatory or governance context**: Any constraints — insurance rate filing, fair lending, model risk management, audit requirements, or none
- **Deployment constraints**: Latency, retraining frequency, monobank/production environment limits, or none

If the target type or interpretability requirement is missing, ask one clarifying question — these two inputs drive the entire recommendation.

## Instructions

### Step 1: Classify the Prediction Problem
Pin down the problem type before naming any model. Map to one of these families:
- **Continuous outcome** → regression family (linear, GLM Gamma/Tweedie, quantile, GAM, tree ensembles)
- **Binary/probability outcome** → classification family (logistic GLM, penalized logistic, gradient boosting, GAM)
- **Count outcome** → count family (Poisson, Negative Binomial, zero-inflated, Tweedie for frequency-severity)
- **Time-to-event with censoring** → survival family (Kaplan-Meier, Cox PH, AFT, discrete-time hazard, survival GBM)
- **Multiclass/ordinal** → multinomial or ordinal logistic, softmax GBM
- **Grouped/hierarchical data** → mixed-effects / GLMM, or credibility-weighted approaches

Explicitly name which family applies and why, based on the target type stated. If the data is panel/grouped or heavily censored, flag that it overrides the naive choice (e.g., "counts with excess zeros → zero-inflated, not plain Poisson").

### Step 2: Apply the Interpretability Filter
Cut the candidate space by the stated interpretability requirement:
- **Fully transparent required** (rate filings, fair lending, regulated pricing): Restrict to GLMs, GAMs with monotonic constraints, penalized regression, decision trees with bounded depth. Exclude unconstrained ensembles.
- **Partially interpretable acceptable**: Allow gradient boosting / random forests **with** SHAP or partial dependence, monotonic-constrained GBMs, and Explainable Boosting Machines (EBM/GA2M).
- **Black box acceptable**: Open the full space — XGBoost/LightGBM/CatBoost, neural nets — but still rank by data-fit, not just accuracy ceiling.

State which candidates were eliminated by this filter and why. In regulated insurance contexts, note that a monotonic-constrained GBM or GLM+GAM is often the practical ceiling.

### Step 3: Match to Dataset Characteristics
Refine the shortlist against the data profile:
- **Small n / high dimensionality**: Favor penalized regression (Lasso/Ridge/Elastic Net) over deep ensembles; ensembles overfit small samples
- **Large n, many interactions**: Favor gradient boosting — captures nonlinearity the analyst won't specify manually
- **Heavy class imbalance**: Note resampling, class weights, or focal loss; recommend PR-AUC over ROC-AUC as the evaluation metric
- **High-cardinality categoricals**: Favor CatBoost / target encoding / GLM with credibility grouping over one-hot into a linear model
- **Missingness**: If informative, recommend explicit missing-indicator or native-NA handling (LightGBM/XGBoost); if MAR, note imputation strategy
- **Censoring/truncation** (insurance claims, policy duration): Enforce survival methods — a standard regressor on observed durations is biased

### Step 4: Produce the Ranked Shortlist (3-5 Candidates)
For each candidate, provide a structured block:
1. **Model**: Named method (e.g., "Tweedie GLM," "Cox Proportional Hazards," "Monotonic-constrained LightGBM")
2. **Fit rationale**: 2 sentences on why it suits *this* problem and data
3. **Key assumptions**: The assumptions that must hold (e.g., proportional hazards, log-link variance structure, independence) and how to check each
4. **Interpretability level**: What the analyst can explain to a stakeholder with this model
5. **Watch-outs**: The specific failure mode for this pairing (e.g., "PH assumption violated for time-varying covariates")

Rank by best overall fit given the constraints — not by raw predictive power. Put the recommended primary first.

### Step 5: Recommend Validation Strategy
Specify how to test the chosen approach honestly:
- **Split scheme**: Random k-fold vs. time-based holdout (mandatory for temporal drift) vs. grouped CV (mandatory for panel data to prevent leakage)
- **Evaluation metric matched to target**: RMSE/MAE (continuous), deviance/log-loss (GLM), PR-AUC (imbalanced binary), C-index / integrated Brier score (survival), Gini/lift (insurance ranking)
- **Assumption diagnostics**: The specific checks for the recommended model (residual plots, PH tests, calibration curves, dispersion test)
- **Overfitting guard**: Early stopping, regularization path, or nested CV for hyperparameter tuning

### Step 6: Name the Fallback
State the second-choice approach and the trigger to switch to it — e.g., "If PH tests fail, move from Cox to an AFT or discrete-time hazard model," or "If the GLM underfits nonlinear interactions, escalate to a monotonic-constrained GBM with SHAP for the filing narrative."

## Output Format
- **Structure**: Problem classification → interpretability filter → data-fit refinement → ranked shortlist (structured blocks) → validation strategy → fallback
- **Length**: 900–1,300 words
- **Format**: Structured document — prose for reasoning, numbered blocks for the shortlist, bulleted lists for validation and diagnostics
- **Sections**: Problem Classification, Interpretability Filter, Data-Fit Refinement, Ranked Model Shortlist, Validation Strategy, Fallback Plan
- **Tone**: Senior quantitative analyst — precise, assumption-aware, decisive without overclaiming; names methods exactly and refuses to hand-wave over trade-offs

## Quality Checklist
Before delivering output, verify:
- [ ] The prediction target type is explicitly classified into a model family before any method is named
- [ ] 3–5 candidate models are provided, each with fit rationale, assumptions, interpretability level, and a specific watch-out
- [ ] Candidates eliminated by the interpretability filter are named, with the reason stated
- [ ] The evaluation metric recommended matches the target type (no ROC-AUC for continuous, no RMSE for survival)
- [ ] Censoring, imbalance, grouping, or small-n conditions in the input actually change the recommendation — not ignored
- [ ] A named fallback and a concrete switch trigger are provided

## Notes
- In regulated insurance and lending contexts, a monotonic-constrained GBM with SHAP or a well-specified GLM/GAM is usually the practical interpretability ceiling — recommend accuracy gains that survive the filing, not the leaderboard.
- Censoring is the most common silently-broken assumption: any time-to-event target with incomplete follow-up must go to a survival method, even if the analyst framed it as regression.
- When frequency and severity both matter (claims modeling), consider whether a single Tweedie GLM or a two-part frequency-severity split serves the business decision better — this depends on whether the two components need separate governance.
- Prefer the simplest model that meets the accuracy and interpretability bar; escalate model complexity only when a diagnostic shows the simpler class is underfitting.

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