Autocarver MCP server

<!-- mcp-name: io.github.mdefrance/autocarver -->

AutoCarver turns raw numeric, categorical, and ordinal columns into optimal, drift-robust, human-readable bins in a few lines of code. Stop losing model performance to suboptimal manual binning — and stop discovering overfit bins in production monitoring.

• Provably optimal — exhaustive search: for a fixed minfreq, maxn_mod and metric (Tschuprow's T by default, or Cramér's V), no other admissible bin combination scores higher. It checked them all so you don't have to.
• Robust by construction — every candidate grouping is vetoed unless it holds on a held-out dev set (and optional CV folds), at fit time rather than in monitoring.
• Define → carve → model — declare your Features, fit a carver, transform: the whole feature set is carved in one supervised pass, not one notebook per feature. One carver per target type — BinaryCarver, MulticlassCarver, OrdinalCarver, ContinuousCarver (regression) — all with the identical API.
• AI-assisted — a local MCP server lets your LLM assistant qualify and carve columns through tool calls, fully on your machine.

On the Titanic quick start, Fare collapses from 72 pre-carving modalities to 2 bins while its association with survival rises: Tschuprow's T 0.18 raw → 0.29 carved.

Built for credit scoring, fraud detection, and risk modeling.

🆕 What's New

📊 Cross-validated robustness. fit now accepts a cv argument for extra held-out robustness views on top of (or instead of) a dev set: carver.fit(X, y, cv=5). Accepts an int, any scikit-learn splitter, or explicit index pairs, resolved via sklearn.modelselection.checkcv — folds veto over-fit combinations but never reorder them (ranks stay anchored to the full train set). See Cross-validation folds.

Shortened. The full README is on GitHub.