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Easy benchmarking of machine learning models with sklearn interface with statistical tests built-in.
Bumps [pyyaml](https://github.com/yaml/pyyaml) from 5.1.1 to 5.4. Changelog Sourced from pyyaml's changelog. 5.4 (2021-01-19) yaml/pyyaml#407 -- Build modernization, remove distutils, fix metadata, build wheels, CI to GHA yaml/pyyaml#472 -- Fix for...
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot] avatar"){kind=avatar}
The nominal coverage of precision recall curves doesn't match the 95% specified level in boot_test.py. We must set ``x_grid = np.linspace(0.05, 0.95, DEFAULT_NGRID)`` to get the tests to pass. We...
There are already lots of randomized inputs in the tests, this can be made cleaner using the hypothesis package: [http://hypothesis.readthedocs.io/en/latest/index.html](url) We should also make an effort to randomize input types,...
Add the loss function for accuracy-at-k where multiple predictions are provided. The correctness of the decision rule can be tested against `classification.hard_loss` with the appropriate (large) loss matrix.
Allow export of mark down in `sciprint` in addition to latex and plain text.
These tests should just be an adaptation of those for classification.py
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Easy benchmarking of machine learning models with sklearn interface with statistical tests built-in.