Editing Decision tree learning (section)

===Implementations===
Many data mining software packages provide implementations of one or more decision tree algorithms (e.g. random forest).

Open source examples include:

* [[ALGLIB]], a C++, C# and Java numerical analysis library with data analysis features (random forest)
* [[KNIME]], a free and open-source data analytics, reporting and integration platform (decision trees, random forest)
* [[Orange (software)|Orange]], an open-source data visualization, machine learning and data mining toolkit (random forest)
* [[R (programming language)|R]] (an open-source software environment for statistical computing, which includes several CART implementations such as rpart, party and randomForest packages),
*
* [[scikit-learn]] (a free and open-source machine learning library for the [[Python (programming language)|Python]] programming language).
* [[Weka (machine learning)|Weka]] (a free and open-source data-mining suite, contains many decision tree algorithms),

Notable commercial software:

* [[MATLAB]],
* [[Microsoft SQL Server]], and
* [[RapidMiner]],
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* [[SAS (software)#Components|SAS Enterprise Miner]],
* [[SPSS Modeler|IBM SPSS Modeler]],