AutoCarver 7.2.8

Automatic Discretization of Features with Optimal Target Association

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.

Install

pip install autocarver

Quick Start

Binary classification on the Titanic dataset:

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"],
    quantitatives=["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("titanic_carver.json")
# carver = BinaryCarver.load("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 — 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.