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As of v0.6, neuropredict now supports regression applications
i.e. predicting continuous targets (in addition to categorical
classes), as well as allow you to regress out covariates /
confounds within the nested-CV (following all the best practices).
Utilizing this feature requires the input datasets be specified in
the pyradigm data structures: code @ https://github.com/raamana/pyradigm,
docs @ https://raamana.github.io/pyradigm/. Check the changelog below for more details.
Older 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.
neuropredict, the tool, is part of a broader initiative described below
to develop easy, comprehensive and standardized predictive analysis:
Citation
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). neuropredict: easy machine learning and standardized predictive analysis of biomarkers (Version 0.4.5). Zenodo. https://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
Change Log - version 0.6
Major feature: Ability to predict continuous variables (regression)
Major feature: Ability to handle confounds (regress them out, augmenting etc)
Redesigned the internal structure for easier extensibility
New CVResults class for easier management of a wealth of outputs generated in the Classification and Regression workflows
API access is refreshed and easier
Change Log - version 0.5.2
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
About
Easy and comprehensive assessment of predictive power, with support for neuroimaging features