Enhancing Predictive Analytics Effectiveness with Evolutionary AutoML Pipelines

Machine learning (ML) has achieved considerable success in recent years, and many disciplines rely on it.With Automated Machine Learning (AutoML) tools, organizations can unlock valuable new business insights, embed advanced AI capabilities in applications, and empower data scientists and nontechnical experts to build predictive models rapidly.AutoML tools are within broader MLOps (Machine Learning Operations) platforms, such as Oracle AutoML (OML4Py) or pure Python frameworks like FEDOT.We have built a simplified AutoML pipeline, focusing on hyperparameter optimization, based on the Optimal Multiple Kernel-Support Vector Machine (OMK-SVM) method.A benchmarking experiment was conducted to identify customers with a higher likelihood of switching from one streaming service to another movie streaming provider.The results revealed that our approach delivers best-in-class performance (SVM), and our evolutionary approach to hyperparameter optimization provides results comparable to those of the FEDOT framework.