Statistical analysis of the performance of four Apache Spark ML algorithms

Genaro Camele; Waldo Hasperué; Franco Ronchetti; Facundo Manuel Quiroga

doi:10.24215/16666038.22.e14

Authors

Genaro Camele III-LIDI, Facultad de Informática, Universidad Nacional de La Plata https://orcid.org/0000-0001-6979-9103
Waldo Hasperué III-LIDI, Facultad de Informática, Universidad Nacional de La Plata https://orcid.org/0000-0002-9950-1563
Franco Ronchetti III-LIDI, Facultad de Informática, Universidad Nacional de La Plata https://orcid.org/0000-0003-3173-1327
Facundo Manuel Quiroga III-LIDI, Facultad de Informática, Universidad Nacional de La Plata https://orcid.org/0000-0003-4495-4327

DOI:

https://doi.org/10.24215/16666038.22.e14

Keywords:

Big Data, Machine Learning, Classification Models, Apache Spark, Spark ML, Wilcoxon Test, Student’s T Test

Abstract

Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess the
importance of each feature for a particular task. However, due to the increasing size of currently available
databases, distributed processing has become a necessity for many tasks. In this context, the Apache Spark
ML library is one of the most widely used libraries for performing classification and other tasks with large
datasets. Therefore, knowing both the predictive performance and efficiency of its main algorithms before
applying a FS technique is crucial to planning computations and saving time. In this work, a comparative
study of four Spark ML classification algorithms is carried out, statistically measuring execution times and
predictive power based on the number of attributes from a colon cancer database. Results were statistically analyzed, showing that, although Random Forest and Na¨ıve Bayes are the algorithms with the shortest execution times, Support Vector Machine obtains models with the best predictive power. The study of the performance of these algorithms is interesting as they are applied in many different problems, such as classification of pathologies from epigenomic data, image classification, prediction of computer attacks in network security problems, among others.

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