What is a potential outcome of a model that is too complex and has high variance?

A model that is too complex and exhibits high variance is characterized by its sensitivity to fluctuations in the training data, which can lead to a phenomenon known as overfitting. When a model overfits, it learns not only the underlying patterns in the training data but also the noise and outliers, making it perform exceptionally well on the training dataset while struggling to generalize to new, unseen data.

This behavior results in a model that may show high accuracy during training but fails to deliver reliable predictions in practical applications, as it does not capture the essential characteristics of the broader dataset. Thus, overfitting is a critical concern in machine learning, especially when managing the complexity of models, as it can lead to misleading assessments of a model’s efficacy if only the training performance is considered.

In contrast, simpler models might generalize better but risk underfitting, which means they fail to capture relevant patterns in the data. Therefore, the recognition that excessive complexity correlates with overfitting is fundamental in both developing effective machine learning models and ensuring they function well in real-world situations.