doubt 6.0.0

Bringing back uncertainty to machine learning.

Doubt

Bringing back uncertainty to machine learning.

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A Python package to include prediction intervals in the predictions of machine learning models, to quantify their uncertainty.

Installation

You can install doubt with pip:

pip install doubt

If you want to be able to use the preprocessed regression datasets as well, you install it with the datasets extra:

pip install doubt[datasets]

Features

• Bootstrap wrapper for all Scikit-Learn models
  • Can also be used to calculate usual bootstrapped statistics of a dataset
• Quantile Regression for all generalised linear models
• Quantile Regression Forests
• A uniform dataset API, with 24 regression datasets and counting

Quick Start

If you already have a model in Scikit-Learn, then you can simply wrap it in a Boot to enable predicting with prediction intervals:

>>> from sklearn.linear_model import LinearRegression
>>> from doubt import Boot
>>> from doubt.datasets import PowerPlant
>>>
>>> X, y = PowerPlant().split()
>>> clf = Boot(LinearRegression())
>>> clf = clf.fit(X, y)
>>> clf.predict([10, 30, 1000, 50], uncertainty=0.05)
(481.9203102126274, array([473.43314309, 490.0313962 ]))

Alternatively, you can use one of the standalone models with uncertainty outputs. For instance, a QuantileRegressionForest:

>>> from doubt import QuantileRegressionForest as QRF
>>> from doubt.datasets import Concrete
>>> import numpy as np
>>>
>>> X, y = Concrete().split()
>>> clf = QRF(max_leaf_nodes=8)
>>> clf.fit(X, y)
>>> clf.predict(np.ones(8), uncertainty=0.25)
(16.933590347847982, array([ 8.93456428, 26.0664534 ]))

Citation

@inproceedings{mougannielsen2023monitoring,
  title={Monitoring Model Deterioration with Explainable Uncertainty Estimation via Non-parametric Bootstrap},
  author={Mougan, Carlos and Nielsen, Dan Saattrup},
  booktitle={AAAI Conference on Artificial Intelligence},
  year={2023}
}