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https://github.com/raamana/neuropredict
Easy and comprehensive assessment of predictive power, with support for neuroimaging features
https://github.com/raamana/neuropredict
anatomical-mri cross-validation easy-to-use functional-connectivity machine-learning neuroimaging nilearn pattern-recognition report resting-state scikit-learn structural-imaging tract-based-statistics tractography
Last synced: 11 days ago
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Easy and comprehensive assessment of predictive power, with support for neuroimaging features
- Host: GitHub
- URL: https://github.com/raamana/neuropredict
- Owner: raamana
- License: apache-2.0
- Created: 2017-03-02T20:53:39.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-03-21T12:12:37.000Z (2 months ago)
- Last Synced: 2024-04-21T18:10:58.303Z (about 1 month ago)
- Topics: anatomical-mri, cross-validation, easy-to-use, functional-connectivity, machine-learning, neuroimaging, nilearn, pattern-recognition, report, resting-state, scikit-learn, structural-imaging, tract-based-statistics, tractography
- Language: Python
- Homepage: https://raamana.github.io/neuropredict/
- Size: 16.4 MB
- Stars: 100
- Watchers: 10
- Forks: 26
- Open Issues: 29
-
Metadata Files:
- Readme: README.rst
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Lists
- AI - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learnings - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-advanced-metering-infrastructure - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
- awesome-python-machine-learning - neuropredict - Easy and comprehensive assessment of predictive power, with support for neuroimaging features. (Uncategorized / Uncategorized)
- awesome-machine-learning - neuropredict - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. (Python / General-Purpose Machine Learning)
README
.. image:: docs/logo_neuropredict.png
:width: 150.. image:: https://img.shields.io/badge/say-thanks-ff69b4.svg
:target: https://saythanks.io/to/raamana**Documentation**: https://raamana.github.io/neuropredict/
News
----- 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.Overview
--------On a high level,
.. image:: docs/high_level_flow.png
:alt: roleofneuropredictOn a more detailed level,
.. image:: docs/role.png
:alt: roleofneuropredict- Docs: https://raamana.github.io/neuropredict/
- Contributors most welcome: `check ideas `__ and the following
`guidelines `__.
Thanks.Long term goals
---------------neuropredict, the tool, is part of a broader initiative described below
to develop easy, comprehensive and standardized predictive analysis:.. image:: docs/neuropredict_long_term_goals.jpg
:alt: longtermgoalsCitation
--------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. 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.00382Change 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 easierChange 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.. |logo| image:: docs/logo_neuropredict.png
.. |travis| image:: https://travis-ci.org/raamana/neuropredict.svg?branch=master
:target: https://travis-ci.org/raamana/neuropredict.svg?branch=master
.. |Code Health| image:: https://landscape.io/github/raamana/neuropredict/master/landscape.svg?style=flat
:target: https://landscape.io/github/raamana/neuropredict/master
.. |Codacy Badge| image:: https://api.codacy.com/project/badge/Grade/501e560b8a424562a1b8f7cd2f3cadfe
:target: https://www.codacy.com/app/raamana/neuropredict?utm_source=github.com&utm_medium=referral&utm_content=raamana/neuropredict&utm_campaign=Badge_Grade
.. |PyPI version| image:: https://badge.fury.io/py/neuropredict.svg
:target: https://badge.fury.io/py/neuropredict
.. |Python versions| image:: https://img.shields.io/badge/python-3.5%2C%203.6-blue.svg
.. |saythanks| image:: https://img.shields.io/badge/say-thanks-ff69b4.svg
:target: https://saythanks.io/to/raamana