scikit-learn: machine learning in Python — scikit-learn 1.8.0 …
Preprocessing Feature extraction and normalization. Applications: Transforming input data such as text for use with machine learning algorithms. Algorithms: Preprocessing, feature …
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 · Supervised learning- Linear Models- Ordinary Least Squares, Ridge regression and classification, Lasso, Multi-task Lasso, Elastic-Net, Multi-task Elastic-Net, Least Angle …
1. Supervised learning — scikit-learn 1.8.0 documentation
Jan 1, 2010 · 1. Supervised learning # 1.1. Linear Models 1.1.1. Ordinary Least Squares 1.1.2. Ridge regression and classification 1.1.3. Lasso 1.1.4. Multi-task Lasso 1.1.5. Elastic-Net …
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 …
An introduction to machine learning with scikit-learn
Machine learning is about learning some properties of a data set and then testing those properties against another data set. A common practice in machine learning is to evaluate an algorithm …
About us — scikit-learn 1.8.0 documentation
scikit-learn is a community project, developed by a large group of people, all across the world. A few core contributor teams, listed below, have central roles, however a more complete list of …
scikit-learn Tutorials — scikit-learn 1.4.2 documentation
, An introduction to machine learning with scikit-learn- Machine learning: the problem setting, Loading an example dataset, Learning and predicting, Conventions., A tutorial on statistical …
3.1. Cross-validation: evaluating estimator performance — scikit …
While i.i.d. data is a common assumption in machine learning theory, it rarely holds in practice. If one knows that the samples have been generated using a time-dependent process, it is safer …
1.12. Multiclass and multioutput algorithms - scikit-learn
This section of the user guide covers functionality related to multi-learning problems, including multiclass, multilabel, and multioutput classification and regression.