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)
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
EVBUS-0.0.5.tar.gz
(4.5 kB
view details)
File details
Details for the file EVBUS-0.0.5.tar.gz.
File metadata
- Download URL: EVBUS-0.0.5.tar.gz
- Upload date:
- Size: 4.5 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
feaa6dce8b434ec66cf0d3ba26d87d7cec84296b7999053ee03154d5bb8d8c31
|
|
| MD5 |
b44ac739f3a189d441409a78975db034
|
|
| BLAKE2b-256 |
7dc0f0e27831d7d79ad8fb13351301997819e322c36421fbf188d8d5f95dacda
|
