scikit-learn: machine learning in Python — scikit-learn 1.8.0 …
scikit-learn Machine Learning in Python Getting Started Release Highlights for 1.8
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, …
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. …
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 …
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 …
sklearn — scikit-learn 1.8.0 documentation
sklearn # Configure global settings and get information about the working environment.
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 …
1.10. Decision Trees — scikit-learn 1.8.0 documentation
Alternatively, scikit-learn uses the total sample weighted impurity of the terminal nodes for R (T). As shown above, the impurity of a node depends on the criterion.
1.6. Nearest Neighbors — scikit-learn 1.8.0 documentation
In scikit-learn, ball-tree-based neighbors searches are specified using the keyword algorithm = 'ball_tree', and are computed using the class BallTree. Alternatively, the user can work with …
1.13. Feature selection — scikit-learn 1.8.0 documentation
The classes in the sklearn.feature_selection module can be used for feature selection/dimensionality reduction on sample sets, either to improve estimators’ accuracy …