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Scikit-learn: Machine Learning in Python
[article]
2018
arXiv
pre-print
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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 machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. ...
In Table 1 , we compare computation time for a few algorithms implemented in the major machine learning toolkits accessible in Python. ...
arXiv:1201.0490v4
fatcat:rz47i2dguvbuvp2glinpp4fx24
Maintenance & Extension of Scikit-learn: Machine Learning in Python
[article]
2021
figshare.com
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CZI EOSS Cycle 4 grant proposal for scikit-learn ...
Landscape Analysis Scikit-learn is one of the oldest and most widely used libraries for machine learning in Python. ...
Proposal Summary The open-source machine learning project scikit-learn has become a foundation for applied machine learning and data science in academic and industrial research. ...
doi:10.6084/m9.figshare.16528449.v1
fatcat:emnynyh2cvcn3hrrtxemwxnz4a
Pymc-learn: Practical Probabilistic Machine Learning in Python
[article]
2018
arXiv
pre-print
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Pymc-learn is a Python package providing a variety of state-of-the-art probabilistic models for supervised and unsupervised machine learning. ...
It is inspired by scikit-learn and focuses on bringing probabilistic machine learning to non-specialists. It uses a general-purpose high-level language that mimics scikit-learn. ...
Acknowledgments We would like to acknowledge the scikit-learn, pymc3 and pymc3-models communities for open-sourcing their respective Python packages. ...
arXiv:1811.00542v1
fatcat:liryu44hcbhsfooxpopsifsmgi
mvlearn: Multiview Machine Learning in Python
[article]
2021
arXiv
pre-print
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2020 pre-print arXiv:2005.11890v2 fatcat:4th2qyy2j5dwhmhsqal6auinoeOther Versions
Its simple API closely follows that of scikit-learn for increased ease-of-use. ...
However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. ...
6 Acknowledgements This work is supported by the Defense Advanced Research Projects Agency (DARPA) Lifelong Learning Machines program through contract FA8650-18-2-7834 and through funding from Microsoft ...
arXiv:2005.11890v4
fatcat:4rw3rcnwmzdg3nhconzjvxpake
Machine Learning in Python: Main developments and technology trends in data science, machine learning, and artificial intelligence
[article]
2020
arXiv
pre-print
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We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward. ...
This survey offers insight into the field of machine learning with Python, taking a tour through important topics to identify some of the core hardware and software paradigms that have enabled it. ...
Scikit-learn, the Industry Standard for Classical Machine Learning Scikit-learn [19] (Figure 1 ) has become the industry standard Python library used for feature engineering and classical ML modeling ...
arXiv:2002.04803v2
fatcat:lvbczmz7xvbyjhs65zubwluzb4
Scikit-learn
2015
GetMobile Mobile Computing and Communications
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The Python data ecosystem Machine learning is only a small part of a dataanalysis pipeline, and scikit-learn dovetails nicely into the rich Python ecosystem. ...
Here we give a quick introduction to scikit-learn as well as to machine-learning basics. (a programming language to generate compiled code in a Python-like syntax [1] ). ...
doi:10.1145/2786984.2786995
fatcat:rt5h6ei55vdy5c4gzsb4xbqtqe
River: machine learning for streaming data in Python
[article]
2020
arXiv
pre-print
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It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. ...
River is a machine learning library for dynamic data streams and continual learning. ...
It is the result from the merger of the two most popular packages for stream learning in Python: Creme and scikit-multiflow. ...
arXiv:2012.04740v1
fatcat:77ggseogl5ft7ke4kvjnszvl4m
DoubleML – An Object-Oriented Implementation of Double Machine Learning in Python
[article]
2021
arXiv
pre-print
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DoubleML is an open-source Python library implementing the double machine learning framework of Chernozhukov et al. (2018) for a variety of causal models. ...
The package is distributed under the MIT license and relies on core libraries from the scientific Python ecosystem: scikit-learn, numpy, pandas, scipy, statsmodels and joblib. ...
The Python package DoubleML implements DML for partially linear and interactive regression models and is primarily based on the machine learning package scikit-learn (Pedregosa et al., 2011) . ...
arXiv:2104.03220v2
fatcat:igrqhbxk2fahhglccapb42ad64
Machine Learning in Python: Main Developments and Technology Trends in Data Science, Machine Learning, and Artificial Intelligence
2020
Information
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Deep neural networks, along with advancements in classical machine learning and scalable general-purpose graphics processing unit (GPU) computing, have become critical components of artificial intelligence ...
We cover widely-used libraries and concepts, collected together for holistic comparison, with the goal of educating the reader and driving the field of Python machine learning forward. ...
Scikit-learn, the Industry Standard for Classical Machine Learning Scikit-learn [19] (Figure 1 ) has become the industry standard Python library used for feature engineering and classical ML modeling ...
doi:10.3390/info11040193
fatcat:hetp7ngcpbbcpkhdcyowuiiwxe
Kernel methods library for pattern analysis and machine learning in python
[article]
2020
arXiv
pre-print
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Kernel methods have proven to be powerful techniques for pattern analysis and machine learning (ML) in a variety of domains. ...
The kernelmethods library fills that important void in the python ML ecosystem in a domain-agnostic fashion, allowing the sample data type to be anything from numerical, categorical, graphs or a combination ...
. kernelmethods aims to fill this important gap in the python ecosystem, to provide a scikit-learn-like library focused on kernel methods, with the following goals: 1) an intuitive API, 2) deep extensibility ...
arXiv:2005.13483v1
fatcat:sj7usurpfnh6tfo6242lyen3tm
Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
[article]
2016
arXiv
pre-print
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Imbalanced-learn is an open-source python toolbox aiming at providing a wide range of methods to cope with the problem of imbalanced dataset frequently encountered in machine learning and pattern recognition ...
The toolbox is publicly available in GitHub: https://github.com/scikit-learn-contrib/imbalanced-learn. ...
In this paper, we present the imbalanced-learn API, a python toolbox to tackle the curse of imbalanced datasets in machine learning. ...
arXiv:1609.06570v1
fatcat:xjnwlrtawrauhfvwdyyhunmpp4
dalex: Responsible Machine Learning with Interactive Explainability and Fairness in Python
[article]
2021
arXiv
pre-print
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To facilitate the development of responsible machine learning models, we showcase dalex, a Python package which implements the model-agnostic interface for interactive model exploration. ...
It adopts the design crafted through the development of various tools for responsible machine learning; thus, it aims at the unification of the existing solutions. ...
These algorithms are available for broad user-bases through numerous machine learning frameworks in Python like scikit-learn (Pedregosa et al., 2011) , tensorflow (Abadi et al., 2016) , xgboost (Chen ...
arXiv:2012.14406v2
fatcat:yxxfgxm5lrehleu6v6wjcy2hnq
House Price Prediction using Machine Learning in Python
2021
International journal of advanced engineering research and applications
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This paper will give information that how we will predict the home price with the help of different features and python with its libraries. ...
Real estate is a very emerging field in everyone's day to day life. The prices of houses are regularly changing on daily basis and are sometimes fired rather than based on actual estimates. ...
The libraries were imported in Python like scikit learn for implementing machine learning algos. ...
doi:10.46593/ijaera.2021.v07i05.001
fatcat:m7puc2j4erebhhscuwijpgoxca
Efficient and Accurate In-Database Machine Learning with SQL Code Generation in Python
[article]
2021
arXiv
pre-print
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Following an analysis of the advantages of SQL-based Machine Learning (ML) and a short literature survey of the field, we describe a novel method for In-Database Machine Learning (IDBML). ...
We measured accuracy and computation time and compared it to Scikit Learn state of the art classification algorithms. ...
Additionally, our research prototype was only 0.9% less accurate than a decision tree computed by Scikit Learn, a state-of-the-art machine learning toolkit, and only 1.9% less accurate than Scikit's random ...
arXiv:2104.03224v2
fatcat:pfwxgl4frfgwfk25ix4fmvsl5y
sknet: A Python framework for Machine Learning in Complex Networks
2021
Journal of Open Source Software
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Recent advances in Machine Learning, an area that leverages data to identify patterns, and in Complex Networks, an area which leverages connections between entities to identify complex phenomena and can ...
Both of those areas can be related to the task of 'learning' from data (Silva & Zhao, 2016) . ...
., 2008) compatible Python package for machine learning tasks in complex networks. sknet was designed to be used by both researchers and by students in courses on Machine Learning or Complex Networks. ...
doi:10.21105/joss.03864
fatcat:vuxhs4kt6nf7fez6c4l57jw3cu
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