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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.
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 ...
Easy data preparation. Data is prepared by creating a bootstrap set and a certain number of decision trees to build a random forest that also utilizes feature selection, as mentioned in § Random Forests. Random Forests are more complex to implement than lone decision trees or other algorithms.
Fast algorithms such as decision trees are commonly used in ensemble methods (e.g., random forests), although slower algorithms can benefit from ensemble techniques as well. By analogy, ensemble techniques have been used also in unsupervised learning scenarios, for example in consensus clustering or in anomaly detection.
A deeper tree can influence the runtime in a negative way. If a certain classification algorithm is being used, then a deeper tree could mean the runtime of this classification algorithm is significantly slower. There is also the possibility that the actual algorithm building the decision tree will get significantly slower as the tree gets deeper.
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An ensemble of models employing the random subspace method can be constructed using the following algorithm: Let the number of training points be N and the number of features in the training data be D. Let L be the number of individual models in the ensemble. For each individual model l, choose n l (n l < N) to be the number of input points for l.