GradientCobra Python Library

Installation of the library

Gradient Cobra library can be installed from pypi using pip:

pip install gradientcobra

This library contains various aggregation methods for machine learning predictions.

Aggregation methods

• Gradient COBRA is a kernel-based consensual aggregation method for regressoin (S. Has, 2023). GradientCOBRA method can be implemented by:

from gradientcobra.gradientcobra import GradientCOBRA

For more information about this method, read GradientCOBRA documentation.

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• KernelSmoother is a nonparametric regression estimation method. It can be implemented by:

from gradientcobra.gradientcobra import KernelSmoother

For more information about this method, read KernelSmoother documentation.

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• MixCOBRARegressor is an aggregation method for both regression and classification that takes into account both input and output of the basic estimators (A. Fischer and M. Mougeot (2019)). This method is available for regression and can be implemented in python by:

from gradientcobra.mixcobra import MixCOBRARegressor

For more information about this method, read MixCOBRARegressor documentation.

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• SuperLearner is a special case of stack regression method that aggregates regressors based on their predicted features using meta learner (M. J. Van der Laan et al., 2007). This method can be implemented in gradientcobra library by:

from gradientcobra.superlearner import SuperLearner

For more information about this method, read SuperLearner documentation.

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References

• S. Has (2023). A Gradient COBRA: A kernel-based consensual aggregation for regression. Journal of Data Science, Statistics, and Visualisation, 3(2).

• A. Fischer, M. Mougeot (2019). Aggregation using input-output trade-off. Journal of Statistical Planning and Inference, 200.

• G. Biau, A. Fischer, B. Guedj and J. D. Malley (2016), COBRA: A combined regression strategy, Journal of Multivariate Analysis.

• M. Mojirsheibani (1999), Combining Classifiers via Discretization, Journal of the American Statistical Association.

• M. J. Van der Laan, E. C. Polley, and A. E. Hubbard (2007). Super Learner. Statistical Applications of Genetics and Molecular Biology, 6, article 25.

• T. Hastie, R. Tibshirani, J. Friedman (2009). Kernel Smoothing Methods. The Elements of Statistical Learning. Springer Series in Statistics. Springer, New York, NY.