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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.
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 ...
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.
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.
In mathematics and computer science, a random tree is a tree or arborescence that is formed by a stochastic process. Types of random trees include: Types of random trees include: Uniform spanning tree , a spanning tree of a given graph in which each different tree is equally likely to be selected
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.
In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow.
Discussions of some more exotic generalizations of random forests. There are a lot of neat, somewhat exotic models which use random forests as a base, but this has the same risk as a list of links. Significantly more examples, similar to sections 3.3,4.3,5.3,6.3,etc of the Criminisi paper I linked above.