Statistical Analysis of the Performance of Four Apache Spark ML Algorithms

Abstract

Feature selection (FS) techniques generally require repeatedly training and evaluating models to assess the importance of each feature for a particular task. How-ever, due to the increasing size of currently available databases, distributed processing has become a neces-sity 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 compu-tations 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 Naïve Bayes are the algorithms with the short-est 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.