scikit-learn: machine learning in Python — scikit-learn 1.6.0 …
"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]" "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish."
Getting Started — scikit-learn 1.6.0 documentation
Scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities.
Installing scikit-learn — scikit-learn 1.6.0 documentation
Installing scikit-learn# There are different ways to install scikit-learn: Install the latest official release. This is the best approach for most users. It will provide a stable version and pre-built packages are available for most platforms. Install the version of scikit-learn provided by your operating system or Python distribution. This is ...
User Guide — scikit-learn 1.6.0 documentation
2010年1月1日 · 8. Computing with scikit-learn. 8.1. Strategies to scale computationally: bigger data; 8.2. Computational Performance; 8.3. Parallelism, resource management, and configuration; 9. Model persistence; 10. Common pitfalls and recommended practices; 11. Dispatching. 11.1. Array API support (experimental) 12. Choosing the right estimator; 13 ...
Examples — scikit-learn 1.6.0 documentation
This is the gallery of examples that showcase how scikit-learn can be used. Some examples demonstrate the use of the API in general and some demonstrate specific applications in tutorial form. Also check out our user guide for more detailed illustrations.
1. Supervised learning — scikit-learn 1.6.0 documentation
2010年1月1日 · 8. Computing with scikit-learn. 8.1. Strategies to scale computationally: bigger data; 8.2. Computational Performance; 8.3. Parallelism, resource management, and configuration; 9. Model persistence; 10. Common pitfalls and recommended practices; 11. Dispatching. 11.1. Array API support (experimental) 12. Choosing the right estimator; 13 ...
API Reference — scikit-learn 1.6.0 documentation
This is the class and function reference of scikit-learn. Please refer to the full user guide for further details, as the raw specifications of classes and functions may not be enough to give full guidelines on their uses.
scikit-learn: machine learning in Python — scikit-learn 1.0.2 …
"scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish"
12. Choosing the right estimator — scikit-learn 1.6.0 documentation
12. Choosing the right estimator#. Often the hardest part of solving a machine learning problem can be finding the right estimator for the job. Different estimators are better suited for different types of data and different problems.
About us — scikit-learn 1.6.0 documentation
The scikit-learn project has a long history of open source coding sprints with over 50 sprint events from 2010 to present day. There are scores of sponsors who contributed to costs which include venue, food, travel, developer time and more.