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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