scikit-multiflow

scikit-multiflow allows to easily design and run experiments and to extend existing stream learning algorithms. It features a collection of classification, regression, concept drift detection and anomaly detection algorithms. It also includes a set of data stream generators and evaluators. scikit-multiflow is designed to interoperate with Python's numerical and scientific libraries NumPy and SciPy and is compatible with Jupyter Notebooks.

The scikit-multiflow library is implemented under the open research principles and is currently distributed under the BSD 3-clause license. scikit-multiflow is mainly written in Python, and some core elements are written in Cython for performance. scikit-multiflow integrates with other Python libraries such as Matplotlib for plotting, scikit-learn for incremental learning methods{{Cite web|url=https://scikit-learn.org/stable/modules/computing.html?highlight=incremental#incremental-learning|title=scikit-learn — Incremental learning|last=|first=|date=|website=scikit-learn.org|archive-url=|archive-date=|access-date=2020-04-08}} compatible with the stream learning setting, Pandas for data manipulation, Numpy and SciPy.

The scikit-multiflow is composed of the following sub-packages:

• anomaly_detection: anomaly detection methods.
• data: data stream methods including methods for batch-to-stream conversion and generators.
• drift_detection: methods for concept drift detection.
• evaluation: evaluation methods for stream learning.
• lazy: methods in which generalisation of the training data is delayed until a query is received, i.e., neighbours-based methods such as kNN.
• meta: meta learning (also known as ensemble) methods.
• neural_networks: methods based on neural networks.
• prototype: prototype-based learning methods.
• rules: rule-based learning methods.
• transform: perform data transformations.
• trees: tree-based methods, e.g. Hoeffding trees which are a type of decision tree for data streams.

scikit-multiflow started as a collaboration between researchers at Télécom Paris (Institut Polytechnique de Paris{{Cite web|url=https://www.ip-paris.fr/en/home-en/|title=Institut Polytechnique de Paris|last=|first=|date=|language=en-GB|archive-url=|archive-date=|access-date=2020-04-08}}) and École Polytechnique. Development is currently carried by the University of Waikato, Télécom Paris, École Polytechnique and the open research community.

{{Portal|Free and open-source software}}

• Massive Online Analysis (MOA){{Cite journal|last1=Bifet|first1=Albert|last2=Holmes|first2=Geoff|last3=Kirkby|first3=Richard|last4=Pfahringer|first4=Bernhard|date=2010|title=MOA: Massive Online Analysis|url=http://jmlr.org/papers/v11/bifet10a.html|journal=Journal of Machine Learning Research|volume=11|issue=52|pages=1601–1604|issn=1533-7928}}
• MEKA{{Cite journal|last1=Read|first1=Jesse|last2=Reutemann|first2=Peter|last3=Pfahringer|first3=Bernhard|last4=Holmes|first4=Geoff|date=2016|title=MEKA: A Multi-label/Multi-target Extension to WEKA|url=http://jmlr.org/papers/v17/12-164.html|journal=Journal of Machine Learning Research|volume=17|issue=21|pages=1–5|issn=1533-7928}}

{{Reflist|30em}}

• {{Official website|https://scikit-multiflow.github.io/}}
• {{GitHub|scikit-multiflow/scikit-multiflow}}

{{SciPy ecosystem}}

Category:Data mining and machine learning software

Category:Free statistical software

Category:Free software programmed in Python

Category:Python (programming language) scientific libraries