Benchmark test method and device of supervised learning algorithm in distributed environment

Abstract

The embodiment of the invention provides a benchmark test method and device of a supervised learning algorithm in a distributed environment. The method comprises the steps that a first benchmark test result which is determined according to output data in a benchmark test is obtained; a distributed performance index in the benchmark test is obtained, and the distributed performance index is determined as a second benchmark test result; the first benchmark test result and the second benchmark test result are merged to obtain a benchmark test total result. According to a perfect scheme used for solving a benchmark test problem of the supervised learning algorithm in the distributed environment, the benchmark test method and device can assist technical staff to conduct accurate and quick assessment on the performance of the supervised learning algorithm.

Description

technical field [0001] The present application relates to the technical field of machine learning, in particular to a benchmark test method for a supervised learning algorithm in a distributed environment and a benchmark test device for a supervised learning algorithm in a distributed environment. Background technique [0002] Machine learning is a multi-field interdisciplinary subject that has emerged in the past 20 years, involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other disciplines. Machine learning algorithm is a kind of algorithm that automatically analyzes and obtains laws from data, and uses the laws to predict unknown data. [0003] At present, machine learning has a very wide range of applications, such as: data mining, computer vision, natural language processing, biometric recognition, search engines, medical diagnosis, detection of credit card fraud, securities market analysis, DNA sequence sequ...

AI Technical Summary

Problems solved by technology

[0009] Due to the complexity of device deployment in supervised learning in a distributed environment, there are many factors in resource coordination, communication and consumption, which makes the benchmark for supervised learning algorithms in a distributed environment, that is, for distributed It is more difficult to evaluate the performance of supervised learning algorithms in the environment

[0010] At present, no complete and effective solution has been proposed for the benchmarking of supervised learning algorithms in a distributed environment.

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