Regression in Machine Learning

Author(s): Kuriko Iwai

Originally published on Towards AI.

Navigating model complexity and practical frameworks for model selection in regression problems

Regression is a common task in machine learning with variety of applications.

This article explores the intricacies of regression in machine learning, delving into the fundamental challenges of model complexity and the theoretical underpinnings of regression algorithms. It discusses the practical frameworks available for model selection and the significance of generalization bounds, highlighting the importance of aligning model complexity with data quality. Additionally, real-world scenarios are presented to illustrate how different models and approaches impact performance, particularly in cases of limited and abundant data, thereby emphasizing the necessity of optimizing predictive models for effective generalization.

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Published via Towards AI