scikit-learn: machine learning in Python — scikit-learn 1.8.0 …
scikit-learn Machine Learning in Python Getting Started Release Highlights for 1.8
API Reference — scikit-learn 1.8.0 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 …
User Guide — scikit-learn 1.8.0 documentation
Jan 1, 2010 · 9. Computing with scikit-learn 9.1. Strategies to scale computationally: bigger data 9.1.1. Scaling with instances using out-of-core learning 9.2. Computational Performance 9.2.1. Prediction …
Getting Started — scikit-learn 1.8.0 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 …
Installing scikit-learn — scikit-learn 1.8.0 documentation
Install the version of scikit-learn provided by your operating system or Python distribution. This is a quick option for those who have operating systems or Python distributions that distribute scikit-learn.
Examples — scikit-learn 1.8.0 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.
1.17. Neural network models (supervised) - scikit-learn
In particular, scikit-learn offers no GPU support. For much faster, GPU-based implementations, as well as frameworks offering much more flexibility to build deep learning architectures, see Related Projects.
1. Supervised learning — scikit-learn 1.8.0 documentation
Jan 1, 2010 · Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle Regression, LARS Lasso, Orthogonal …
About us — scikit-learn 1.8.0 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, …
2.3. Clustering — scikit-learn 1.8.0 documentation
The current implementation uses ball trees and kd-trees to determine the neighborhood of points, which avoids calculating the full distance matrix (as was done in scikit-learn versions before 0.14). The …