Random forest optimization method and system based on tensor decomposition
A technology of random forest and tensor decomposition, which is applied to computer components, instruments, calculations, etc., can solve the problems of low machine learning prediction efficiency and achieve the effect of improving prediction efficiency
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[0060] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.
[0061] The specific embodiment of the present invention provides a random forest optimization method based on tensor decomposition, which mainly includes the following steps:
[0062] S11. Reading in the training data set as the initial training set;
[0063] S12. Acquiring a new training set based on the initial training set using a preset random sampling method to form a random forest training set, wherein the random forest training set includes training a decision tree model using random subspace technology;
[0064] S13. Construct a tensor model based on the decision tree model in the random f...
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