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
The purpose of this guide is to illustrate some of the main features of scikit-learn. It assumes basic working knowledge of machine learning practices (model fitting, predicting, cross-validation, etc.).
Installing scikit-learn — scikit-learn 1.8.0 documentation
Install the 64-bit version of Python 3, for instance from the official website. Now create a virtual environment (venv) and install scikit-learn. Note that the virtual environment is optional but strongly …
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 …
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. 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 …
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).
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 …
14. External Resources, Videos and Talks - scikit-learn
For those that are still new to the scientific Python ecosystem, we highly recommend the Python Scientific Lecture Notes. This will help you find your footing a bit and will definitely improve your scikit …
LogisticRegression — scikit-learn 1.8.0 documentation
LogisticRegression # class sklearn.linear_model.LogisticRegression(penalty='deprecated', *, C=1.0, l1_ratio=0.0, dual=False, tol=0.0001, fit_intercept=True, intercept_scaling=1, class_weight=None, …