Random Forests in Data Mining

Data analysis and machine learning have become an integrative part of the modern scientific methodology, offering automated procedures for the prediction of a phenomenon based on past observations, unraveling underlying patterns in data and providing insights about the problem. Since always, artificial intelligence has been driven by the ambition to understand and uncover complex relations in data. That is, to find models that can not only produce accurate predictions, but also be used to extract knowledge in an intelligible way. This section introduces random forest description in details. Random Forest Definition A Random Forest consists of a collection or ensemble of simple tree predictors, each capable of producing a response when presented with a set of predictor values. For classification problems, this response takes the form of a class membership, which associates, or classifies, a set of independent predictor values with one of the categories present in the dependent variable. Alternatively, for regression problems, the tree response is an estimate of the dependent variable given the predictors. A Random Forest consists of an arbitrary number of simple trees, which are used to determine the final outcome.  For classification problems, the ensemble of simple trees vote for the most popular class. In…

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