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  2. Random forest - Wikipedia

    en.wikipedia.org/wiki/Random_forest

    Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during training. For classification tasks, the output of the random forest is the class selected by most trees.

  3. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    A decision tree is a simple representation for classifying examples. For this section, assume that all of the input features have finite discrete domains, and there is a single target feature called the "classification". Each element of the domain of the classification is called a class. A decision tree or a classification tree is a tree in ...

  4. Random subspace method - Wikipedia

    en.wikipedia.org/wiki/Random_subspace_method

    The random subspace method has been used for decision trees; when combined with "ordinary" bagging of decision trees, the resulting models are called random forests. [5] It has also been applied to linear classifiers, [6] support vector machines, [7] nearest neighbours [8] [9] and other types of classifiers.

  5. Common Lisp - Wikipedia

    en.wikipedia.org/wiki/Common_Lisp

    Depending on the implementation, the file compiler generates byte-code (for example for the Java Virtual Machine), C language code (which then is compiled with a C compiler) or, directly, native code. Common Lisp implementations can be used interactively, even though the code gets fully compiled.

  6. C4.5 algorithm - Wikipedia

    en.wikipedia.org/wiki/C4.5_algorithm

    C4.5 is an algorithm used to generate a decision tree developed by Ross Quinlan. [1] C4.5 is an extension of Quinlan's earlier ID3 algorithm.The decision trees generated by C4.5 can be used for classification, and for this reason, C4.5 is often referred to as a statistical classifier.

  7. Bootstrap aggregating - Wikipedia

    en.wikipedia.org/wiki/Bootstrap_aggregating

    The random forest classifier operates with a high accuracy and speed. [11] Random forests are much faster than decision trees because of using a smaller dataset. To recreate specific results, it is necessary to keep track of the exact random seed used to generate the bootstrap sets.

  8. Category:Articles with example Java code - Wikipedia

    en.wikipedia.org/wiki/Category:Articles_with...

    Template talk:Java; Java (programming language) Java annotation; Java API for XML Processing; Java class loader; Java collections framework; Java Modeling Language; Java Pathfinder; Java remote method invocation; Java syntax; Jakarta Transactions; Java version history; Template:Java/doc; JavaBeans; JavaFX; JFace; JGroups; Joins (concurrency ...

  9. Foreign function interface - Wikipedia

    en.wikipedia.org/wiki/Foreign_function_interface

    In JNI, for example, C code which "holds on to" object references that it receives from Java must communicate this information successfully to the Java virtual machine or Java Runtime Environment (JRE), otherwise, Java may delete objects before C finishes with them. (The C code must also explicitly release its link to any such object once C has ...