- Good news:
neuropredict
can handle missing data now (that are encoded withnumpy.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