A Method for Optimizing Random Forest Parameters for Machine Learning Model Training

A machine learning model and random forest technology, applied in biological models, computational 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: 2022-03-15
武汉爱科软件技术股份有限公司
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Problems solved by technology

[0004] However, due to the fact that traditional intelligent algorithms can only implement parallel search in a serial manner in a single-processor environment, this method is no longer suitable for the large-scale growth of data sets in the era of big data.

Method used

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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 those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with 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 and figure 2 , a method for optimizing random forest parameters for machine learning model training provided by the invention, is characterized in that, comprises the following steps:

[0029] Step 1: Store the collected training set data in the HDFS distributed file system, and its storage path is the variable path. Taking 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 perform global optimization on the ...

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Abstract

The invention discloses a method for optimizing random forest parameters used in machine learning model training. First, the entire ant population is divided into several subpopulations; Independent evolution; Finally, the migration operator is used to exchange information between subpopulations. Compared with the traditional grid search, the Spark-based parallel antlion algorithm can efficiently find a better parameter combination to improve the classification accuracy of the random forest, and under the big data distributed Spark platform, the optimization calculation speed is fast and the acceleration effect is obvious , which can be used as a next-generation parameter optimizer for cloud computing platforms.

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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Patent Type & Authority Patents(China)
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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