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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. 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 you need to keep track of the exact random seed used to generate the bootstrap sets.

  4. Relevance vector machine - Wikipedia

    en.wikipedia.org/wiki/Relevance_vector_machine

    In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. [1]

  5. pip (package manager) - Wikipedia

    en.wikipedia.org/wiki/Pip_(package_manager)

    An output of pip install virtualenv. Pip's command-line interface allows the install of Python software packages by issuing a command: pip install some-package-name. Users can also remove the package by issuing a command: pip uninstall some-package-name. pip has a feature to manage full lists of packages and corresponding version numbers ...

  6. 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.

  7. Decision tree - Wikipedia

    en.wikipedia.org/wiki/Decision_tree

    The bootstrapped dataset helps remove the bias that occurs when building a decision tree model with the same data the model is tested with. The ability to leverage the power of random forests can also help significantly improve the overall accuracy of the model being built. This method generates many decisions from many decision trees and ...

  8. Jackknife variance estimates for random forest - Wikipedia

    en.wikipedia.org/wiki/Jackknife_Variance...

    In statistics, jackknife variance estimates for random forest are a way to estimate the variance in random forest models, in order to eliminate the bootstrap effects.

  9. Out-of-bag error - Wikipedia

    en.wikipedia.org/wiki/Out-of-bag_error

    When this process is repeated, such as when building a random forest, many bootstrap samples and OOB sets are created. The OOB sets can be aggregated into one dataset, but each sample is only considered out-of-bag for the trees that do not include it in their bootstrap sample.