trust-free 0.9.0

Transparent, Robust & Ultra-Sparse Trees (TRUST) - Free Version

trust

Transparent, Robust & Ultra-Sparse Trees (TRUST) - Free Version

This package provides the free version of our TRUST algorithm, a SOTA interpretable machine learning model.

Installation

You can install this package using pip:

pip install trust

Note: This package includes a precompiled binary and is currently only compatible with macOS 11+ on ARM64 architecture.

Usage

Here are two basic examples of how to use the TRUST algorithm:

from trust import TRUST
import pandas as pd
from sklearn import datasets

Diabetes = pd.DataFrame(datasets.load_diabetes().data)
Diabetes.columns = datasets.load_diabetes().feature_names
diab_target = datasets.load_diabetes().target
Diabetes.insert(len(Diabetes.columns), "Disease_marker", diab_target)
Diabetes_X = Diabetes.iloc[:,:-1]
Diabetes_y = Diabetes.iloc[:,-1]
RLT_Diabetes = TRUST()
RLT_Diabetes.fit(Diabetes_X,Diabetes_y)
y_pred_TRUST = RLT_Diabetes.predict(Diabetes_X)

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error

# Generate synthetic regression data
X, y, coefs = make_regression(n_samples=5000, n_features=20, n_informative=10, coef=True, noise=0.1, random_state=123)
coefs
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)
# Instantiate and fit your model
model = TRUST()
model.fit(X_train, y_train)
# Predict and print results
y_pred = model.predict(X_test)
print("Predictions:", y_pred[:5])
print("True y values:", y_test[:5])
print("test R^2:", r2_score(y_test, y_pred))
model.explain(X_test[0,:], y_pred[0], actual=y_test[0]) 
model.varImp(X_test, y_test, model, corAnalysis=True)

License

This software is provided under a Proprietary - Permissive Binary Only license. For detailed terms, please refer to the LICENSE file included with the distribution.

More Information

For more details, documentation, and information about the full (paid) version of the TRUST algorithm, please visit our official website:

https://adc-trust-ai.github.io/trust/

Further details can be found in our preprint on arxiv:

https://www.arxiv.org/abs/2506.15791