AutoCarver automates supervised feature discretization (binning) to maximize statistical association with your target — using Tschuprow's T or Cramér's V — and validates the chosen bins against a held-out dev set. It supports binary classification, multiclass classification, and regression, and is widely used 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.model_selection.check_cv — folds veto over-fit combinations but never reorder them (ranks stay anchored to the full train set). See Cross-validation folds.

🤖 LLM & MCP integration. AutoCarver now ships a local Model Context Protocol server: point an MCP-aware assistant (VS Code Copilot, Claude Desktop, Cursor, …) at a data file and let it qualify the columns and carve them against your target through tool calls. The server runs fully on your machine — your dataset is never sent to AutoCarver or any external service (only your own LLM provider sees what the assistant shares). Carving quality depends on the LLM, so have a human confirm the feature definitions before production use. See the LLM & MCP guide.

pip install "autocarver[mcp]"

Install

pip install autocarver

Quick Start

Binary classification on the Titanic dataset:

from pathlib import Path

import pandas as pd
from sklearn.model_selection import train_test_split

from AutoCarver import BinaryCarver, Features

# 1. Load data
url = "https://web.stanford.edu/class/archive/cs/cs109/cs109.1166/stuff/titanic.csv"
data = pd.read_csv(url)
target = "Survived"

# 2. Train / dev split, stratified on the target
train, dev = train_test_split(data, test_size=0.33, random_state=42, stratify=data[target])

# 3. Declare features by type
features = Features(
    categoricals=["Sex"],
    numericals=["Age", "Fare", "Siblings/Spouses Aboard", "Parents/Children Aboard"],
    ordinals={"Pclass": ["1", "2", "3"]},
)

# 4. Fit the carver (dev set drives the robustness checks)
carver = BinaryCarver(features=features, min_freq=0.05, max_n_mod=5)
train_processed = carver.fit_transform(train, train[target], X_dev=dev, y_dev=dev[target])
dev_processed = carver.transform(dev)

# 5. Inspect the carved buckets, target rate, and association
print(carver.summary)

# 6. Persist for later use
carver.save(Path("titanic_carver.json"))
# carver = BinaryCarver.load(Path("titanic_carver.json"))

For multiclass classification use MulticlassCarver; for regression use ContinuousCarver — the API is identical. To pre-select features by target association and inter-feature redundancy, pipe the carved output through ClassificationSelector or RegressionSelector.

Why AutoCarver?

• Optimal supervised binning — exhaustive search over admissible bin combinations maximizes Tschuprow's T (default) or Cramér's V. For fixed min_freq, max_n_mod and metric, no other combination scores higher.
• Robust to data drift — every candidate bin combination is validated on a dev set, rejecting any whose target rates flip or whose buckets fall below min_freq.
• First-class ordinal features — OrdinalDiscretizer enforces your declared modality order, so under-represented levels are merged with their nearest neighbour instead of being collapsed by frequency.
• Inspect what was carved — features.summary and features.history give you the bin definitions, per-bin target rate / frequency, and the full carving trace right off the fitted carver.
• Interpretable buckets — human-readable boundaries you can audit, document, and ship to a scorecard.
• Dimensionality reduction — groups under-represented modalities and caps bins per feature (max_n_mod), which is especially useful before one-hot encoding.
• Feature pre-selection — ClassificationSelector / RegressionSelector rank features by target association and filter on inter-feature correlation.

How does it compare?

	AutoCarver	optbinning	sklearn KBinsDiscretizer
Supervised (uses y)	yes	yes	no
Algorithm	exhaustive search over admissible combinations	mixed-integer program (CBC)	quantile / uniform / k-means
Optimality for given min_freq / max_n_mod / metric	guaranteed — best of every admissible combination	provably optimal under MIP constraints	n/a — no target objective
Target types	binary, multiclass, continuous	binary, multiclass, continuous	n/a
Numeric and categorical and ordinal in one fit	yes	one binner per feature	numeric only
Ordinal features with enforced order	yes — OrdinalDiscretizer preserves your declared order	via user_splits workaround (loses ordering)	no
NaN handled as its own modality	yes	yes	no (raises)
Held-out dev-set robustness check	yes — dev set + optional k-fold CV, built into fit	no (script CV yourself)	no
Per-bin stats + carving history after fit	features.summary, features.history	binning_table	no
JSON round-trip persistence	yes (carver.save("...json"))	via pickle	via pickle
sklearn Pipeline compatible	yes	yes	yes
Feature pre-selection helpers	ClassificationSelector, RegressionSelector	no	no

Side-by-side runnable snippets and a "when to pick which" guide live on the comparison page.

Documentation

Full reference, tutorials, and end-to-end notebook examples on ReadTheDocs.