Getting Started

Install zoneboost and fit your first model in a few lines.

Installation

zoneboost is currently published on TestPyPI only (not yet on real PyPI):

pip install -i https://test.pypi.org/simple/ zoneboost

zoneboost has three runtime dependencies — numpy, pandas, and scikit-learn — and no compiled extensions, so installation is a plain wheel install on any platform.

Quickstart

Both estimators accept a pandas DataFrame directly. Declare which columns are nominal categories with categorical_features; everything else is treated as continuous and split into data-driven zones automatically.

Regression

import pandas as pd
from zoneboost import ZoneBoostRegressor

X = pd.DataFrame({
    "rooms": [3, 4, 2, 5, 3, 4, 2, 5],
    "distance_km": [5.0, 2.0, 8.0, 1.0, 6.0, 3.0, 7.5, 1.5],
    "neighborhood": ["a", "b", "a", "b", "a", "b", "a", "b"],
})
y = [300, 450, 220, 520, 310, 470, 230, 510]

model = ZoneBoostRegressor(
    categorical_features=["neighborhood"], random_state=0
)
model.fit(X, y)
model.predict(X)

Classification

from zoneboost import ZoneBoostClassifier

y_class = [0, 1, 0, 1, 0, 1, 0, 1]
clf = ZoneBoostClassifier(
    categorical_features=["neighborhood"], random_state=0
)
clf.fit(X, y_class)
clf.predict_proba(X)   # (n_samples, n_classes), rows sum to 1
clf.predict(X)          # works for binary and 3+ classes (native multinomial) alike

Both continuous and categorical columns accept NaN/None directly — no imputation step needed before calling fit. See Missing Values for how that's handled internally.

Requirements

Dependency	Minimum version
Python	`>=3.9`
numpy	`>=1.23`
pandas	`>=1.5`
scikit-learn	`>=1.2`

Development

Working on zoneboost itself:

pip install -e ".[dev]"
pytest

Source and issue tracker: github.com/stainaz/zoneboost.