Scikit-learn: Machine Learning in Python
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Summary
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, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
- Type
- preprint
- Published
- 2011-02-01
- Cited by
- 93,558
- References
- 17
- Access
- Open access
- OpenAlex
- https://openalex.org/W2101234009
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10659969
Keywords
Python (programming language), Documentation, Computer science, MIT License, Artificial intelligence
References
- Five Balltree Construction Algorithms
- A supervised clustering approach for fMRI-based inference of brain states
- A Randomized Algorithm for Principal Component Analysis
- Modular Toolkit for Data Processing (MDP): A Python Data Processing Framework
- Regularization Paths for Generalized Linear Models via Coordinate Descent
- PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data
- LIBLINEAR: A Library for Large Linear Classification
- The NumPy Array: A Structure for Efficient Numerical Computation
- Result Analysis of the NIPS 2003 Feature Selection Challenge
- LIBSVM: A library for support vector machines
- Python: Batteries Included
- Guest Editor's Introduction: Python: Batteries Included
- Least angle regression
- The SHOGUN Machine Learning Toolbox
- PyBrain
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- Easy Hyperparameter Search Using Optunity
- Exudate detection in color retinal images for mass screening of diabetic retinopathy
- Which fMRI clustering gives good brain parcellations?
- Detection of temporal lobe epilepsy using support vector machines in multi-parametric quantitative MR imaging
- Monitoring Pharmacologically Induced Immunosuppression by Immune Repertoire Sequencing to Detect Acute Allograft Rejection in Heart Transplant Patients: A Proof-of-Concept Diagnostic Accuracy Study
- Classifiers for Ischemic Stroke Lesion Segmentation: A Comparison Study
- Complex Human Activity Recognition Using Smartphone and Wrist-Worn Motion Sensors
- Conformal prediction to define applicability domain – A case study on predicting ER and AR binding
- Learning statistical models of phenotypes using noisy labeled training data
- Leveraging the Information from Markov State Models To Improve the Convergence of Umbrella Sampling Simulations.
- Systematic Analysis of Transcriptional and Post-transcriptional Regulation of Metabolism in Yeast
- Detection of Side Chain Rearrangements Mediating the Motions of Transmembrane Helices in Molecular Dynamics Simulations of G Protein-Coupled Receptors
- Coupling between protein stability and catalytic activity determines pathogenicity of G6PD variants
- Context-aware system for pre-triggering irreversible vehicle safety actuators.
- Alignment-Based Prediction of Sites of Metabolism
- Joint prediction of multiple scores captures better individual traits from brain images
- Predicting Treatment Relations with Semantic Patterns over Biomedical Knowledge Graphs
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