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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.
E-mail spam problem is a common classification problem, in this problem, 57 features are used to classify spam e-mail and non-spam e-mail. Applying IJ-U variance formula to evaluate the accuracy of models with m=15,19 and 57.
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.
Because three of the four predict the positive class, the ensemble's overall classification is positive. Random forests like the one shown are a common application of bagging. An example of the aggregation process for an ensemble of decision trees. Individual classifications are aggregated, and an overall classification is derived.
Tin Kam Ho (Chinese: 何天琴) is a computer scientist at IBM Research with contributions to machine learning, data mining, and classification.Ho is noted for introducing random decision forests in 1995, and for her pioneering work in ensemble learning and data complexity analysis.
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.
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 tallies up the votes from each decision tree to make the final classification.
Luchman, J.N.; CHAIDFOREST: Stata module to conduct random forest ensemble classification based on chi-square automated interaction detection (CHAID) as base learner, Available for free download, or type within Stata: ssc install chaidforest. IBM SPSS Decision Trees grows exhaustive CHAID trees as well as a few other types of trees such as CART.