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 …
7.3. Preprocessing data — scikit-learn 1.8.0 documentation
Standardization of datasets is a common requirement for many machine learning estimators implemented in scikit-learn; they might behave badly if the individual features do not more or …
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 …
1.11. Ensembles: Gradient boosting, random forests ... - scikit-learn
In contrast to the original publication [B2001], the scikit-learn implementation combines classifiers by averaging their probabilistic prediction, instead of letting each classifier vote for a single class.
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 …
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.