In assessing the generalizability of a statistical learning algorithm, it is vital to consider a variety of diverse, feature-rich datasets. In this package, we develop a simple interface to many common benchmark datasets, including the Penn Machine Learning Benchmarks Olson (2017) <arXiv:1703.00512>, UC-Irvine Machine-Learning Repository, and MNIST Lecun (1998) <doi:10.1109/5.726791> for both classification and regression tasks, allowing users to examine performance across many disparate contexts. Utilities are also included to automatically load and clean data from different datasets.
|Maintainer||Eric Bridgeford <[email protected]>|
|Package repository||View on GitHub|
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