Simple Model Choices — pick a sensible baseline and iterate

Start simple. A modest, interpretable model plus a strong baseline often solves problems faster and with less organizational friction than a complex black box. This guide helps you choose a first model and know when to consider more powerful but less transparent approaches.

Step 1 — Define your baseline

Before building a model, identify the simplest rule or human process that will serve as your baseline. Examples: a threshold on a single metric, a last-known-state rule, or a subject-matter expert list. Always measure new models against this baseline.

Step 2 — Prefer interpretable models first

• Logistic regression (binary outcomes): Good when relationships are roughly linear, and you need feature weights that people can read.
• Decision trees: Useful when rules are non-linear and decision makers want a rule-like explanation.
• Linear regression (continuous outcomes): Good for estimating quantities and understanding directionality.

Step 3 — When to try ensembles and more powerful learners

If simple models fail to reach business-acceptable performance, try tree-based ensembles (random forest, gradient boosted trees). They typically perform better with heterogeneous features but add complexity:

• Trade-off: Better predictive power vs. harder explanations and heavier maintenance.
• Mitigation: Use SHAP or partial-dependence plots to explain drivers, and lock feature sets to reduce instability.

Step 4 — Calibration and thresholds

Predictive scores must map to decisions. Calibrate model probabilities (e.g., isotonic or Platt scaling) and choose operating thresholds based on the business cost of false positives vs false negatives rather than raw accuracy.

Step 5 — Minimum sample size and feature guidance

There is no single magic number for sample size; instead, start with these heuristics:

• If labels are rare, you need proportionally more data—consider stratified sampling or targeted labeling.
• Always validate with a holdout period representative of current operations.

Step 6 — Production-friendly considerations

• Prefer models that can be recomputed quickly at inference time given your infrastructure and decision cadence.
• Keep feature transformations simple and reproducible in pipelines (avoid ad-hoc notebook logic).
• Record model versioning metadata and expected input feature schema.

Common mistakes

• Skipping the baseline comparison and assuming higher model metrics are meaningful.
• Using extremely complex models when a few actionable rules suffice.
• Building a model optimized for a metric that isn't tied to the actual decision or cost structure.

Start with a baseline rule, an interpretable model, and a clear thresholding strategy. Only graduate to more complex learners when you can demonstrate material business improvement and have governance for explanation and monitoring.