Scikit-learn: Machine Learning in Python
PedregosaFabian,VaroquauxGaël,GramfortAlexandre,MichelVincent,ThirionBertrand,GriselOlivier,BlondelMathieu,PrettenhoferPeter,WeissRon,DubourgVincent,VanderplasJake,PassosAlexandre,CournapeauDavid,BrucherMatthieu,PerrotMatthieu,DuchesnayÉdouard +15 more
TLDR
Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.Abstract:
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 bringing mach...read more
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Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Journal ArticleDOI
DBSCAN Revisited, Revisited: Why and How You Should (Still) Use DBSCAN
TL;DR: In new experiments, it is shown that the new SIGMOD 2015 methods do not appear to offer practical benefits if the DBSCAN parameters are well chosen and thus they are primarily of theoretical interest.
Journal ArticleDOI
Learning a model of facial shape and expression from 4D scans
TL;DR: Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method.
Proceedings ArticleDOI
NILMTK: an open source toolkit for non-intrusive load monitoring
Nipun Batra,John Kelly,Oliver Parson,Haimonti Dutta,William J. Knottenbelt,Alex Rogers,Amarjeet Singh,Mani Srivastava +7 more
TL;DR: This work is the first research to compare multiple disaggregation approaches across multiple publicly available data sets, and demonstrates the range of reproducible analyses made possible by the toolkit.
Journal ArticleDOI
A Tutorial on Multilabel Learning
Eva Gibaja,Sebastián Ventura +1 more
TL;DR: An up-to-date tutorial about multilabel learning is presented that introduces the paradigm and describes the main contributions developed and Evaluation measures, fields of application, trending topics, and resources are presented.