  Short Message Service (SMS) is a very popular service used for communication
by mobile users. However, this popular service can be abused by executing
illegal activities and influencing security risks. Nowadays, many automatic
machine learning (AutoML) tools exist which can help domain experts and lay
users to build high-quality ML models with little or no machine learning
knowledge. In this work, a classification performance comparison was conducted
between three automatic ML tools for SMS spam message filtering. These tools
are mljar-supervised AutoML, H2O AutoML, and Tree-based Pipeline Optimization
Tool (TPOT) AutoML. Experimental results showed that ensemble models achieved
the best classification performance. The Stacked Ensemble model, which was
built using H2O AutoML, achieved the best performance in terms of Log Loss
(0.8370), true positive (1088/1116), and true negative (281/287) metrics. There
is a 19.05\% improvement in Log Loss with respect to TPOT AutoML and 5.56\%
improvement with respect to mljar-supervised AutoML. The satisfactory filtering
performance achieved with AutoML tools provides a potential application for
AutoML tools to automatically determine the best ML model that can perform best
for SMS spam message filtering.
