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A sinuous journey through ``tensor_forest``

Posted by Sophia Lu on Dec 11, 2017 11:45:30 AM

Random forest, an ensemble method

The random forest (RF) model, first proposed by Tin Kam Ho in 1995, is a subclass of ensemble learning methods that is applied to classification and regression. An ensemble method constructs a set of classifiers – a group of decision trees, in the case of RF – and determines the label for each data instance by taking the weighted average of each classifier’s output.

The learning algorithm utilizes the divide-and-conquer approach and reduces the inherent variance of a single instance of the model through bootstrapping. Therefore, “ensembling” a group of weaker classifiers boosts the performance and the resulting aggregated classifier is a stronger model.

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Topics: Data Science, machine learning, AI, tensor forest, tensorflow


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