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
- scikit-survival
Survival analysis built on top of scikit-learn
Not published by PyPI - scispacy
A full SpaCy pipeline and models for scientific/biomedical documents.
Not published by PyPI - scrubadub
Clean personally identifiable information from dirty dirty text.
Not published by PyPI - scrublet
Doublet prediction in single-cell RNA-sequencing data
Not published by PyPI - scvi-tools
Deep probabilistic analysis of single-cell omics data.
Not published by PyPI - sdgx
synthetic-data-generator
Not published by PyPI - sdmetrics
Metrics for Synthetic Data Generation Projects
Not published by PyPI - secretflow
SecretFlow
Not published by PyPI - secretflow-lite
SecretFlow Lite
Not published by PyPI - segment-geospatial-uni
Meta AI' Segment Anything Model (SAM) for Geospatial Data.
Not published by PyPI - segregation
Analytics for spatial and non-spatial segregation in Python.
Not published by PyPI - semantic-link-sempy
Semantic link for Microsoft Fabric
Not published by PyPI
Latest news the 0 latest events that mention it
No published news event mentions scikit-learn yet.