Technology · Library
scikit-learn
Library · Catalog tech@1
Machine learning in Python.
- Matched as
- scikit-learn
- sklearn
Momentum
Each signal is ranked as a percentile among the technologies that have it. A signal without enough history is not counted, and never shown as zero. How momentum works
Momentum
Collecting0 of 5 signalsNo score yet. Momentum is published only when at least 2 of the 5 signals have history. scikit-learn has 0, so there is no single number: each signal is shown on its own below. It is not a zero, and nothing is estimated in its place.
Snapshot OCT 24, 2026 · Scoring tech@1
- Official repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.67,452 stars now
- Member repository stars
- Collecting
- Star growth needs repository snapshots 30 days apart.0 new members in 30 days
- Package adoption
- Not published by its registries
- Its packages’ registries publish no adoption figure, so there is nothing to grow.
- Open models
- No official models
- No official model on Hugging Face, so this signal does not apply.
- News
- Collecting
- News counts once there are 3 events across the last 60 days; it has 0.0 in the last 30 days · 0 in the 30 before
Adoption by registry
The growth of scikit-learn’s own packages, one row per registry in that registry’s own unit. Registries count different things over different windows, so they are never added together.
No registry has two observations of scikit-learn’s own packages about 30 days apart yet, so there is no growth to show. Nothing is estimated in its place.
News activity
- Events · 7 days
- 0published events that mention it
- Events · 30 days
- 0published events that mention it
- Previous 30 days
- 0events in the 30 days before
Momentum history
Momentum history starts OCT 8, 2026. A chart appears once two daily snapshots carry a score; none does so far.
Repositories
Official 1
- scikit-learn/scikit-learn
scikit-learn: machine learning in Python
67.5K stars
Members 62repositories tagged with one of its aliases, most-starred first
- flwrlabs/flower
Flower: A Friendly Federated AI Framework
7.2K stars
- interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
7K stars
- biolab/orange3
🍊 :bar_chart: :bulb: Orange: Interactive data analysis
5.7K stars
- dusty-nv/jetson-containers
Machine Learning Containers for NVIDIA Jetson and JetPack-L4T
4.9K stars
- DistrictDataLabs/yellowbrick
Visual analysis and diagnostic tools to facilitate machine learning model selection.
4.4K stars
- microsoft/FLAML
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
4.4K stars
- astroautomata/PySR
Symbolic Learning in Python and Julia
3.8K stars
- christianversloot/machine-learning-articles
🧠💬 Articles I wrote about machine learning, archived from MachineCurve.com.
3.7K stars
- microsoft/hummingbird
Hummingbird compiles trained ML models into tensor computation for faster inference.
3.5K stars
- fnnx-ai/scikit-llm
Seamlessly integrate LLMs into scikit-learn.
3.5K stars
- mljar/mljar-supervised
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
3.3K stars
- nidhaloff/igel
a delightful machine learning tool that allows you to train, test, and use models without writing code
3.1K stars
Packages
“Is” packages are scikit-learn itself; ecosystem packages depend on them. The two are counted separately and never added together. Adoption is each registry’s own figure, with its percentile within that registry.
Is 1the technology itself, most-adopted first
- scikit-learn
A set of python modules for machine learning and data mining
Not published by PyPI
Ecosystem 392packages that depend on it, most-adopted first
- optbinning
OptBinning: The Python Optimal Binning library
Not published by PyPI - orange3
Orange, a component-based data mining framework.
Not published by PyPI - orion-ml
Orion is a machine learning library built for unsupervised time series anomaly detection.
Not published by PyPI - pacmap
The official implementation for PaCMAP: Pairwise Controlled Manifold Approximation Projection
Not published by PyPI - pacmap-dev
The official implementation for PaCMAP: Pairwise Controlled Manifold Approximation Projection
Not published by PyPI - paddleclas
A treasure chest for visual recognition powered by PaddlePaddle.
Not published by PyPI - paddleformers
Easy-to-use and powerful NLP library with Awesome model zoo, supporting wide-range of NLP tasks from research to industrial applications, including Neural Search, Question Answering, Information Extraction and Sentiment Analysis end-to-end system.
Not published by PyPI - paddlespeech
Speech tools and models based on Paddlepaddle
Not published by PyPI - pai-easycv
An all-in-one toolkit for computer vision
Not published by PyPI - pathway
Pathway Live Data Framework is a data processing framework which takes care of streaming data updates for you.
Not published by PyPI - pgmpy
Python Toolkit for Causal and Probabilistic Reasoning
Not published by PyPI - pgmpy-no-torch
A library for Probabilistic Graphical Models
Not published by PyPI
Latest news the 0 latest events that mention it
No published news event mentions scikit-learn yet.