linear-tree
A python library to build Model Trees with Linear Models at the leaves.
Overview
Linear Model Trees combine the learning ability of Decision Tree with the predictive and explicative power of Linear Models. Like in tree-based algorithms, the data are split according to simple decision rules. The goodness of slits is evaluated in gain terms fitting Linear Models in the nodes. This implies that the models in the leaves are linear instead of constant approximations like in classical Decision Trees.
linear-tree is developed to be fully integrable with scikit-learn. LinearTreeRegressor and LinearTreeClassifier are provided as scikit-learn BaseEstimator. They are wrappers that build a decision tree on the data fitting a linear estimator from sklearn.linear_model. All the models available in sklearn.linear_model can be used as linear estimators.
Installation
pip install linear-tree
The module depends on NumPy, SciPy and Scikit-Learn (>=0.23.0). Python 3.6 or above is supported.
Media
- Linear Tree: the perfect mix of Linear Model and Decision Tree
- Model Tree: handle Data Shifts mixing Linear Model and Decision Tree
- Explainable AI with Linear Trees
Usage
Regression
from sklearn.linear_model import LinearRegression
from lineartree import LinearTreeRegressor
from sklearn.datasets import make_regression
X, y = make_regression(n_samples=100, n_features=4,
n_informative=2, n_targets=1,
random_state=0, shuffle=False)
regr = LinearTreeRegressor(base_estimator=LinearRegression())
regr.fit(X, y)
Classification
from sklearn.linear_model import RidgeClassifier
from lineartree import LinearTreeClassifier
from sklearn.datasets import make_classification
X, y = make_classification(n_samples=100, n_features=4,
n_informative=2, n_redundant=0,
random_state=0, shuffle=False)
clf = LinearTreeClassifier(base_estimator=RidgeClassifier())
clf.fit(X, y)
More examples in the notebooks folder.
Check the API Reference to see the parameter configurations and the available methods.
Examples
Show the model tree structure:
Linear Tree Regressor at work:
Linear Tree Classifier at work:
Extract and examine coefficients at the leaves:
1 Jun 30, 2022
36 Oct 19, 2022
268 Dec 01, 2022
2 Jan 24, 2022
47 Nov 25, 2022
47 Jan 01, 2023
28 Dec 16, 2022
781 Dec 25, 2022
75 Nov 27, 2022
1k Jan 08, 2023
26 Dec 03, 2022
16 Nov 09, 2022
43 Dec 12, 2022
26 Nov 28, 2022
2 Aug 11, 2022
127 Jan 04, 2023
583 Dec 30, 2022
2 Feb 03, 2022
31 Oct 07, 2022
585 Oct 12, 2022