Application of Hyperparameter Algorithms using Big Data Platforms

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Published Sep 20, 2021
Venkata daya sagar Ketaraju

Abstract

Algorithms in each step of data analytics application include hyperparameters that are independent of the data itself. The choice of hyperparameters is one of the most time-consuming parts of data analytics since it cannot be performed precisely without heuristic or empirical methods. This paper implemented hyperparameter selection algorithms: Simulated Annealing, Bayesian Search, Tree-structured Parzen Estimators, Differential Evolution, Basin Hopping on Spark, distributed big data processing platform. The performance is measured by comparing the results we get from each algorithm with the results of the Random Search algorithm. We have tested the scalability and ability for parallelization of algorithms.

How to Cite

Ketaraju, V. daya sagar. (2021). Application of Hyperparameter Algorithms using Big Data Platforms. SPAST Abstracts, 1(01). Retrieved from https://spast.org/techrep/article/view/223
Abstract 44 |

Article Details

Keywords

basin hopping, scalability, bigdata, random search

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Section
GE3- Computers & Information Technology