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• Docs: https://raamana.github.io/neuropredict/

• Good news: neuropredict can handle missing data now (that are encoded with numpy.NaN). This is done respecting the cross-validation splits without any data leakage.

On a high level,

On a more detailed level,

• Docs: https://raamana.github.io/neuropredict/
• Contributors welcome here with these guidelines. Thanks.

neuropredict, the tool, is part of a broader initiative described below to develop easy, comprehensive and standardized predictive analysis:

If neuropredict helped you in your research in one way or another, please consider citing one or more of the following, which were essential building blocks of neuropredict:

• Pradeep Reddy Raamana. (2017, November 18). neuropredict: easy machine learning and standardized predictive analysis of biomarkers (Version 0.4.5). Zenodo. http://doi.org/10.5281/zenodo.1058993
• Raamana et al, (2017), Python class defining a machine learning dataset ensuring key-based correspondence and maintaining integrity, Journal of Open Source Software, 2(17), 382, doi:10.21105/joss.00382

• Imputation of missing values
• Additional classifiers such as XGBoost, Decision Trees
• Better internal code structure
• Lot more tests
• More precise tests, as we vary number of classes wildly in test suites
• several bug fixes and enhancements
• More cmd line options such as --print_options from a previous run