B: When a model is too simple to capture patterns in the data

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

When a Model Is Too Simple to Capture Patterns in the Data: Avoiding Underfitting in Machine Learning In the world of machine learning, model performance hinges not only on data quality and quantity but also on the model’s complexity. One common issue developers face is underfitting—a situation where a model is too simple to capture the underlying patterns in the data. ### What Is Underfitting? Underfitting occurs when a model fails to learn the relationships within training data due to insufficient complexity. Unlike overfitting—where a model memorizes noise and performs well on training data but poorly on new inputs—underfitting results in poor performance across both training and test datasets. Simple models, such as linear regression applied to nonlinear data, often exemplify this challenge.