Scikit-Multiflow

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Scikit-multiflow is a machine learning package for streaming data in Python, enabling incremental and adaptive learning. It supports various stream learning tasks like classification, regression, and concept drift detection.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is scikit-multiflow?

Scikit-multiflow is a Python-based machine learning package specifically designed for handling streaming data. It facilitates incremental learning, where models are continuously updated, making it suitable for big data applications requiring real-time responses. The package also incorporates adaptive learning methods to robustly handle concept drift, which are changes in data distribution over time. It offers a range of stream learning tools including data generators, multi-output/multi-target methods, and evaluation techniques. The project is open-source and developed by an active community, though active development has transitioned to the River project, which merges scikit-multiflow with creme.

Best used for

Ideal for data scientists and developers who need to build machine learning models for streaming data, detect concept drift in dynamic environments, and perform real-time predictions. Especially valuable for applications requiring continuous model updates and efficient resource handling.

Common actions

process streaming data

implement machine learning

detect concept drift

perform adaptive learning

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Capabilities

Key features

Incremental learning
Adaptive learning
Resource-wise efficient
Stream learning tools
Concept drift detection
Multi-output/multi-target methods

Target Audience

data scientistdeveloper

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

Is scikit-multiflow still actively developed?

Active development for scikit-multiflow has transitioned to the new River project, which is a merger of scikit-multiflow and creme. While scikit-multiflow remains available, new features and primary development efforts are now focused on River.

What types of machine learning tasks does scikit-multiflow support?

Scikit-multiflow supports various stream learning tasks, including supervised learning (classification and regression for single or multi-output), concept drift detection, and unsupervised learning such as anomaly detection. It's designed for dynamic data streams.

How does scikit-multiflow handle changes in data distribution?

Scikit-multiflow incorporates adaptive learning methods specifically designed to be robust against concept drift, which are changes in data distribution. It includes drift detection methods that can raise alarms and help learning methods adapt to these changes in evolving data streams.

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