MLCompare: Platform to Compare Machine Learning Models

Abstract

Abstract: The field of machine learning (ML) continues to advance, the demand for efficient and effective methods for model development and selection becomes increasingly pressing. Automated Machine Learning (AutoML) platforms have emerged as powerful tools to address this need by automating the process of model selection, hyperparameter tuning, and feature engineering. By integrating a wide array of ML techniques and leveraging cloud computing resources, the platform enables researchers and practitioners to efficiently explore and compare the performance of various algorithms across diverse datasets. Key features include a user-friendly interface, a robust evaluation framework encompassing traditional metrics and advanced techniques, and parallel processing capabilities. Through experiments on benchmark datasets from multiple domains, the platform demonstrates its ability to streamline algorithm selection and provide valuable insights into algorithm performance. This contribution advances the field of AutoML by facilitating informed decision-making and accelerating innovation in ML applications.