Society of Actuaries (SOA) PA Practice Exam 2025 – 400 Free Practice Questions to Pass the Exam

Reducing overfitting has a significant positive impact on model performance, particularly in the context of generalization. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise, leading it to perform very well on training data but poorly on unseen data. By focusing on reducing overfitting, the model becomes simpler and thus can generalize better to new data.

Simpler models tend to capture the essential relationships within the data without being overly complex. This means they are less likely to follow the peculiarities of the training data and more likely to reflect true underlying trends. Such models balance bias and variance effectively, resulting in improved predictive performance on data that the model has not encountered before.

This contrasts with the other options, where increasing bias may not necessarily be beneficial, high predictive accuracy might not be maintained if a model is overly complex, and ensuring all observations are used does not guarantee a model’s efficacy in terms of predicting new outcomes. Reducing overfitting thus serves to create models that can handle real-world data more effectively by prioritizing generalization over fitting to training data noise.