A method for optimizing random forest parameters for machine learning model training

A machine learning model and random forest technology, applied in biological models, computing models, instruments, etc., can solve problems such as unsuitable data sets, and achieve the effects of improving classification accuracy, obvious acceleration effect, and fast calculation speed

Active Publication Date: 2019-06-04
武汉爱科软件技术股份有限公司
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[0004] However, due to the fact that traditional intelligent algorithms can only implement parallel search in a serial manner in

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  • A method for optimizing random forest parameters for machine learning model training
  • A method for optimizing random forest parameters for machine learning model training
  • A method for optimizing random forest parameters for machine learning model training

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[0027] In order to facilitate the understanding and implementation of the present invention by those of ordinary skill in the art, the present invention will be further described in detail with reference to the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0028] Please see figure 1 with figure 2 The method for optimizing random forest parameters for machine learning model training provided by the present invention is characterized in that it includes the following steps:

[0029] Step 1: Store the collected training set data in the HDFS distributed file system. The storage path is variable path. Take LendingClub, a credit loan company in the United States, as an example. Its loan data can be obtained from the official website;

[0030] Step 2: Use the Antlion algorithm to optimize the parameters of the random...

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Abstract

The invention discloses a method for optimizing random forest parameters for machine learning model training. The method comprises the steps that firstly, dividing the whole ant population into a plurality of sub-populations; Then, allowing each sub-population to correspond to one partition in the RDD, and designating independent evolution in one partition; And finally, exchanging information among the sub-populations by utilizing a migration operator. Compared with the traditional grid search, the parallel ant lion algorithm based on the Spark can efficiently find out a better parameter combination to improve the classification precision of the random forest, and under a big data distributed Spark platform, the optimization calculation speed is high, the acceleration effect is obvious, and the parallel ant lion algorithm based on the Spark can be used as a next generation parameter optimizer of a cloud computing platform.

Description

technical field [0001] The invention belongs to the field of large-scale machine learning model training, and relates to a method for optimizing random forest parameters for machine learning model training, in particular to a Spark-based parallel antlion algorithm for machine learning model training to optimize random forests parameter method. Background technique [0002] In recent years, with the rapid growth of information data, machine learning models have been widely promoted and applied. However, as the most critical part of model training, model parameter optimization has always been a difficult problem, and it often takes a long time for engineering. The solution process of classical optimization methods will lead to relatively large time complexity or space complexity, and the final parameter optimization level is closely related to the function form of the problem to be solved. As a random search algorithm imitating the behavior of biological groups, swarm intell...

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Application Information

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IPC IPC(8): G06K9/62G06N3/00
Inventor 陈宏伟常鹏阳胡周符恒侯乔严灵毓宗欣露徐慧
Owner 武汉爱科软件技术股份有限公司
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