forestfire-ml 0.5.1

Tree-based learning in Rust with a Python API.

forestfire

forestfire is a Python package for tree-based learning backed by a Rust core.

Current capabilities:

• decision trees
• random forests
• gradient boosting
• classification and regression
• sklearn-compatible estimator wrappers
• optimized inference runtimes
• model introspection and export

Example:

import numpy as np

from forestfire import train

X = np.array([[0.0], [0.0], [1.0], [1.0]])
y = np.array([0.0, 0.0, 1.0, 1.0])

model = train(X, y, task="classification", tree_type="cart")
print(model.predict(X))
print(model.predict_proba(X))

Sklearn-style wrappers are also available:

from forestfire.tree import CARTClassifier
from forestfire.forest import CARTRandomForestRegressor
from forestfire.gbm import ObliviousGBMRegressor

Common missing-value markers such as None, np.nan, pandas/NumPy NaN, and polars nulls are handled automatically during training and prediction.

The source repository, documentation, and issue tracker live at:

• https://github.com/wsperat/forest-fire