scikit-learn is a free, open-source Python module for machine learning built on top of SciPy, offering simple and efficient tools for classification, regression, clustering, and more.
What is scikit-learn?
scikit-learn is a free, open-source Python module for machine learning built on top of SciPy, offering simple and efficient tools for classification, regression, clustering, and more.
SubcategoryMachine Learning Library
Version1.9.0
PriceFree
What is scikit-learn?
Introduction to scikit-learn — what it does and who it is for.
What do you need to run scikit-learn?
System requirements, OS support, and hardware needs for scikit-learn.
scikit-learn requires Python 3.11 or later along with dependencies such as NumPy, SciPy, joblib, and threadpoolctl; it runs cross-platform on Windows, macOS, and Linux.
| Operating system | Cross-platform (WindowsLinuxmacOS |
|---|---|
| Devices | Desktop/Server (any system running Python) |
Programming languages
- ✓C#
- ✓Cython
- ✓Python
How much does scikit-learn cost?
Pricing plans, license type, and availability for scikit-learn.
scikit-learn is free and open source, distributed under the BSD-3-Clause license at no cost.
| Price summary | Free (Open Source) |
|---|---|
| License details | BSD-3-Clause (3-Clause BSD) license, a permissive open-source license allowing commercial use |
| Availability | Available |
What is scikit-learn used for?
Key features, use cases, and capabilities of scikit-learn.
scikit-learn provides tools for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Key features
- ✓Classification
- ✓Regression
- ✓Clustering
- ✓Dimensionality Reduction
- ✓Model Selection
- ✓Preprocessing
Use cases
- ✓Spam detection and image recognition (classification)
- ✓Predicting drug response or stock prices (regression)
- ✓Customer segmentation and grouping experiment outcomes (clustering)
- ✓Data visualization and feature reduction (dimensionality reduction)
Who should use scikit-learn?
Target users, industries, and ideal use cases for scikit-learn.
scikit-learn is aimed at data scientists, machine learning engineers, researchers, and Python developers building predictive data analysis models.
| Target audience | Data Scientists, Machine Learning Engineers, Researchers, And Python Developers |
|---|
How is scikit-learn deployed?
Deployment options, API, and hosting for scikit-learn.
As an open-source Python library, scikit-learn is self-hosted by installing it via pip or conda directly into your own Python environment.
| API integration | ✓ |
|---|---|
| Open source | ✓ |
| Self-hosting | ✓ |
How do you get started with scikit-learn?
Installation and onboarding steps for scikit-learn.
Get started by installing scikit-learn with 'pip install -U scikit-learn' and following the official user guide and documentation at scikit-learn.org.
What are the pros and cons of scikit-learn?
Balanced review of strengths and weaknesses of scikit-learn.
According to G2 reviews, users praise scikit-learn's ease of use, comprehensive algorithms, and documentation, while citing limited deep learning support and slower performance on large datasets as drawbacks.
| Pro | Con |
|---|---|
| +Ease of use, especially for ML beginners (G2 reviews) | −Limited native deep learning/neural network support compared to TensorFlow/PyTorch (G2 reviews) |
| +Comprehensive, pre-built implementations of classification, regression, and clustering algorithms (G2 reviews) | −Slower execution on very large datasets compared to specialized alternatives (G2 reviews) |
| +Excellent, detailed documentation and tutorials (G2 reviews) | −Handling of categorical variable encoding can be cumbersome (G2 reviews) |
| +Free and open source with active community support (G2 reviews) |
Where can you learn more about scikit-learn?
Documentation, support, and official links for scikit-learn.
Official resources include the scikit-learn documentation, user guide, and the source code repository on GitHub at github.com/scikit-learn/scikit-learn.
| Website | Website |
|---|---|
| Documentation | Documentation |
| Source code | Source code |
| Install | Install |
Frequently asked questions
What is scikit-learn?
scikit-learn is a free, open-source Python module for machine learning built on top of SciPy, offering simple and efficient tools for classification, regression, clustering, and more.
Is scikit-learn free?
scikit-learn is free and open source, distributed under the BSD-3-Clause license at no cost.
What is scikit-learn used for?
scikit-learn provides tools for classification, regression, clustering, dimensionality reduction, model selection, and preprocessing.
Where can I get scikit-learn?
You can get scikit-learn via the official website, the install page, GitHub and the source repository. See the resources section on this page for direct links.
Who should use scikit-learn?
scikit-learn is aimed at data scientists, machine learning engineers, researchers, and Python developers building predictive data analysis models.
What do you need to run scikit-learn?
scikit-learn requires Python 3.11 or later along with dependencies such as NumPy, SciPy, joblib, and threadpoolctl; it runs cross-platform on Windows, macOS, and Linux.
How do you get started with scikit-learn?
Get started by installing scikit-learn with 'pip install -U scikit-learn' and following the official user guide and documentation at scikit-learn.org.
How is scikit-learn deployed?
As an open-source Python library, scikit-learn is self-hosted by installing it via pip or conda directly into your own Python environment.
Who developed scikit-learn?
scikit-learn is developed by scikit-learn community, originally created by David Cournapeau (2007); developed at Inria since 2010; currently primarily funded by Probabl, with support from NumFOCUS and institutional/corporate sponsors.
What are the pros and cons of scikit-learn?
According to G2 reviews, users praise scikit-learn's ease of use, comprehensive algorithms, and documentation, while citing limited deep learning support and slower performance on large datasets as drawbacks.