Hand it rows. Get back a predictor.

Secure and calibrated prediction for applied engineering

CognitiveEstimator is a lightweight tabular regression and Bernoulli-probability calibration Python library designed to streamline the creation of reliable predictors. Instead of forcing the user to manually select model parameters or write a preprocessing pipeline, the library automatically profiles incoming rows, selects appropriate losses, and trains six distinct model families under a shared wall-clock budget. By executing linear models, random forests, XGBoost, TabPFN v2, Gaussian processes, and symbolic regressions in parallel, it identifies the best performing single model or an equal-weighted ensemble, persisting the results through an injected storage adapter.

Designed explicitly for Python developers and ML practitioners who already have tabular data in memory (typically fetched from a database or upstream service), CognitiveEstimator prioritizes correctness, determinism, and calibrated uncertainty over leaderboard-winning heuristics. By using a fixed seed and data fingerprint rather than timestamps, it produces reproducible predictions and handles reload-vs-retrain decisions automatically. The library runs entirely in memory, depends on no global state or service layer, and integrates into existing Python services without a separate model-serving stack.

Key Principles

• Three-Method API: The entire public surface is defined by init, evaluate, and info. Integration is measured in minutes, not days.
• Six Families, One Budget: Seamlessly bridges Linear models, Random Forests, XGBoost, TabPFN v2, Gaussian Processes, and Symbolic Regression through a unified protocol.
• Honest Evaluation & Calibrated Intervals: Uses a leak-proof 70/15/15 split for larger datasets (n ≥ 50), and leave-one-out cross-validation (LOO-CV) for tiny tiers (4–49 rows), returning first-class conformal uncertainty intervals.
• Determinism & Fingerprinting: Same data, seed, and config yields bit-identical outputs. Persistence is delegated through a simple three-method adapter contract (get/put/exists).

Availability

CognitiveEstimator is in early access and not yet self-service: every integration is accompanied, so the fit with your data and constraints is validated first. Write to us to discuss it.

See also

Need a phase-gated workflow for a whole software or MBSE project? See CognitiveSDD.