This article presents a comprehensive framework for mastering model selection in artificial intelligence and machine learning applications across diverse domains. The article addresses the fundamental challenge of selecting models that optimally balance complexity with generalization capability, navigating the classic bias-variance tradeoff that underpins predictive performance. Beginning with theoretical foundations of regularization approaches and complexity measures, the article proceeds through data-driven selection strategies, including cross-validation techniques and advanced hyperparameter optimization methods. The article incorporates robust evaluation metrics for both classification and regression tasks, emphasizing the importance of multi-metric assessment in capturing various performance dimensions. The article extends beyond initial model selection to address the critical yet often overlooked dimension of post-deployment maintenance, including concept drift detection and retraining strategies that ensure sustained model performance over time. The article demonstrates the practical application of these principles in high-stakes environments with domain-specific constraints. The article’s integrated framework offers decision support for strategy selection based on data characteristics, with implementation guidance across common machine learning platforms. By synthesizing theoretical insights with practical considerations, this article provides researchers and practitioners with a structured approach to model selection throughout the complete machine learning lifecycle, ultimately enhancing the reliability and sustainability of AI applications in production environments.
Keywords: Bias-variance tradeoff, Concept drift detection, Hyperparameter optimization, Model selection, Performance evaluation metrics
