**AutoCarver** turns raw numeric, categorical, and ordinal columns into supervised, 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.
- **Searched, not eyeballed** — a dynamic-programming search ranks the candidate groupings by Tschuprow's T (default) or Cramér's V, for your `min_freq` and `max_n_mod`.
- **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.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](https://autocarver.readthedocs.io/en/stable/viability.html#cross-validation-folds).
**🤖 LLM & MCP integration.** AutoCarver now ships a local [Model Context Protocol](https://modelcontextprotocol.io) 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](https://autocarver.readthedocs.io/en/stable/mcp.html).
```bash
pip install "autocarver[mcp]"
```
Once configured, just ask your assistant:
> Qualify the columns in `titanic.csv` and carve them against `Survived`.
The assistant infers feature types, proposes a carving, and returns the summary table — no code written by hand.
Client config
Add to `.vscode/mcp.json` (VS Code / GitHub Copilot) or `claude_desktop_config.json` (Claude Desktop, under `mcpServers` instead of `servers`):
```json
{
"servers": {
"autocarver": {
"command": "python",
"args": ["-m", "AutoCarver.mcp"]
}
}
}
```
If you use [uv](https://docs.astral.sh/uv/), point `command` at `uv` instead so it resolves the environment for you:
```json
{
"servers": {
"autocarver": {
"command": "uv",
"args": ["run", "python", "-m", "AutoCarver.mcp"]
}
}
}
```
## Install
```bash
pip install autocarver
```
## Quick Start
[](https://colab.research.google.com/github/mdefrance/AutoCarver/blob/main/docs/source/examples/quick_start_colab.ipynb)
You already have a DataFrame and a target — that's the first box ticked before you start:
- [x] Load data
- [ ] Split train / dev
- [ ] Declare features by type
- [ ] Fit the carver, validated on the dev set
- [ ] Inspect the carved bins
- [ ] Persist
The rest is the snippet below — binary classification on the Titanic dataset:
```python
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)
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
carver.summary
# 6. Persist for later use
carver.save(Path("titanic_carver.json"))
# 7. Load the carver back in
carver = BinaryCarver.load(Path("titanic_carver.json"))
dev_processed = carver.transform(dev)
```
`min_freq` and `max_n_mod` are the only two knobs that matter to start with — the defaults (`0.02` / `5`) reflect common scoring practice, and every behavioral toggle lives in one `ProcessingConfig` object. Scan, adjust, move on.
For multiclass classification use `MulticlassCarver` (one binning per feature, against the full K-class target) — or `OneVsRestCarver` for a separate binning per class; for ordinal targets use `OrdinalCarver`; 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` with `features=carver.features` (the carver carves its own copy, so your original `features` stay raw).
## What you get
Two questions worth answering before your next model review: can you defend every bin boundary of your current model to a stakeholder — and can you show each one holds on data it has never seen? AutoCarver makes both a one-liner:
- **The best grouping, not the first acceptable one** — candidate groupings are ranked by Tschuprow's T (default) or Cramér's V, and the highest-ranked one that holds on the dev set is kept, so you don't stop at the first grouping that looks fine.
- **Stop silent overfitting before production** — bins that only exist in your training sample degrade quietly under drift. Every candidate combination is validated on a dev set (and optional CV folds): any whose target rates flip or whose buckets fall below `min_freq` is rejected at fit time, not discovered in monitoring.
- **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.
- **You are the final auditor** — `carver.features.summary` and `carver.features.history` expose the bin definitions, per-bin target rate / frequency, and the full carving trace; disagree with a boundary and you can override it, and `transform` applies your fix like any carved bin:
```python
feature = carver.features("Siblings/Spouses Aboard") # any carved feature; labels are [0, 1, 2]
feature.group([1], 2) # merge two bins you consider equivalent
```
- **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.
*Titanic `Age`, one `BinaryCarver.fit` call: 84 raw values collapse to 3 buckets with a monotonic survival rate.*
## How does it compare?
| | **Manual binning** | **AutoCarver** | [**optbinning**](https://github.com/guillermo-navas-palencia/optbinning) | [**sklearn KBinsDiscretizer**](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.KBinsDiscretizer.html) |
| ------------------------------------------------- | ---------------------------------------------------- | ------------------------------------------------------------------ | ------------------------------------------------------------------------ | ------------------------------------------------------------------------------- |
| Algorithm | eyeballing distributions, notebook by notebook | **dynamic-programming search** over ordered groupings | CART pre-binning, then CP solver (CP-SAT default; MIP optional) | quantile / uniform / k-means — unsupervised |
| Grouping kept for given `min_freq` / `max_n_mod` / metric | none — first acceptable grouping wins | **best viable grouping among the top-ranked candidates** | best merge of its CART pre-bins found within a solver time limit | n/a — no target objective |
| Target types | any, at ~1 feature/hour | **binary, multiclass, ordinal, continuous** | binary, multiclass, continuous | n/a |
| All feature types in one `fit` (numeric, categorical, ordinal, `NaN`) | each feature is its own project | **yes — declared ordinal order enforced, `NaN` as its own modality** | yes via `BinningProcess`; no first-class ordinal type (`user_splits` workaround) | numeric only; `NaN` raises |
| Held-out dev-set robustness check | rarely — too tedious to script per feature | **yes — dev set + optional k-fold CV, built into `fit`** | no (script CV yourself) | no |
| Per-bin stats + carving history after `fit` | scattered notebook cells | **`carver.summary`, `carver.history`** | `binning_table` | no |
All three libraries are sklearn-`Pipeline` compatible; AutoCarver adds JSON round-trip persistence (`carver.save("...json")`). AutoCarver (`ClassificationSelector`, `RegressionSelector`) and optbinning (`BinningProcess(selection_criteria=...)`) both offer feature pre-selection. The full feature matrix, side-by-side runnable snippets, and a "when to pick which" guide live on the [comparison page](https://autocarver.readthedocs.io/en/stable/comparison.html).
## Documentation
Full reference, tutorials, and end-to-end notebook examples on [ReadTheDocs](https://autocarver.readthedocs.io/en/stable/index.html).
