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Estimate Variance Based on U-Statistics (EVBUS)

Project description

This is a python implementation of the paper: Mentch, L. & Hooker, G. Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests. J. Mach. Learn. Res. 17, 1–41 (2016).

Installation

pip install EVBUS

Usage

from EVBUS import calculate_variance
from sklearn.datasets import load_boston
import sklearn.model_selection as xval

boston = load_boston()
Y = boston.data[:, 12]
X = boston.data[:, 0:12]

bos_X_train, bos_X_test, bos_y_train, bos_y_test = xval.train_test_split(X, Y, test_size=0.3)

v = calculate_variance(bos_X_train, bos_y_train, bos_X_test, reg=True)
print(v)

Project details


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