D. A method for reducing model complexity

Tue, 05 May 2026 05:23:38 +0000

# A Practical Method for Reducing Model Complexity in Machine Learning In machine learning, model complexity plays a crucial role in determining both performance and efficiency. A model that’s too simple may underfit, failing to capture essential patterns in data, while one that’s overly complex may overfit, memorizing noise instead of learning generalizable insights. Striking the right balance is key to building robust, scalable, and interpretable models. In recent years, researchers have developed a set of effective strategies to reduce model complexity without sacrificing predictive power. One particularly actionable method involves structured regularization combined with feature importance pruning, a technique that offers clarity, computational efficiency, and improved generalization. ## Why Reducing Model Complexity Matters Before diving into the method, it’s important to understand why reducing model complexity is a foundational best practice: