Machine Learning in Python
Identifying which category an object belongs to.
Applications:Spam dectection,image recognition.
Algorithms:Gradient Boosting,nearest neighbors,random forest,logistic regression and more..
Predicting a continuous-valued associated with an object.
Applications:Drug respse,Stock prices.
Algorithms:Gradient Boosting,nearest neigbors,random forest,logistic regression and more..
Automatic grouping of similar object into sets.
Applications:Customer segmentation,grouping experiment outcomes.
Alogorithms:K-Means,HBDSCAN,Hierarchical clustering and more..
Reducing the number of variables to consider.
Applications:Visualization,increased efficiency.
Algorithms:PCA,feature selection,non-negative matrix factorization,and more..
Comparing,validating and choosing parameters and models.
Applications:Improved accuracy via parameter tuning..
Algorithms:Grid search,cross validation,metrics,and more..
Feature extraction and normalization.
Applications:Transformation input data such as text for use with machine learning algorithms.
Algorithms:Preprocessing,feature extraction,and more..
on-going development:scikit-learn 1.6(changelog).
July 2024 scikit-learn 1.5.1 is available for download
May 2024.scikit-learn 1.5.0 is available for download (changelog)
April 2024.scikit-learn 1.4.1 pst1 is available for download(changelog)
Machine LearningAbout us:people and contributing.
More machine learning:Find releate project.
Questions? See FAQ,support,and stackoverflow.
Subscribe to the mailing list.
Blog:blog.scikit-learn.org
logos & branding:logos and branding.
Help us,denote!