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Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringingExpand
The NumPy Array: A Structure for Efficient Numerical Computation
TLDR
This effort shows, NumPy performance can be improved through three techniques: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. Expand
API design for machine learning software: experiences from the scikit-learn project
TLDR
The simple and elegant interface shared by all learning and processing units in the Scikit-learn library is described and its advantages in terms of composition and reusability are discussed. Expand
Machine learning for neuroimaging with scikit-learn
TLDR
It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain. Expand
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments
TLDR
The Brain Imaging Data Structure (BIDS) is developed, a standard for organizing and describing MRI datasets that uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations. Expand
Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
TLDR
The feasibility of inter‐site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi‐site autism dataset is demonstrated. Expand
NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain
TLDR
NeuroVault is a web based repository that allows researchers to store, share, visualize, and decode statistical maps of the human brain and leverages the power of the Neurosynth database to provide cognitive decoding of deposited maps. Expand
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
TLDR
T theory and experiments outline that the popular “leave‐one‐out” strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred, and it can be favorable to use sane defaults, in particular for non‐sparse decoders. Expand
Mayavi: 3D Visualization of Scientific Data
TLDR
Mayavi provides a continuum of tools for developing scientific applications, ranging from interactive and script-based data visualization in Python to full-blown custom end-user applications. Expand
Brain covariance selection: better individual functional connectivity models using population prior
TLDR
This work describes subject-level brain functional connectivity structure as a multivari-ate Gaussian process and introduces a new strategy to estimate it from group data, by imposing a common structure on the graphical model in the population, the first report of a cross-validated model of spontaneous brain activity. Expand
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