What is a benefit of using models that consist of multiple decision trees?

Using models that consist of multiple decision trees, such as ensemble methods like Random Forests or Gradient Boosting, significantly enhances predictive accuracy compared to a single decision tree. This improvement arises from the principle of combining the outputs of multiple models to get a more robust final prediction.

Single decision trees can be prone to overfitting, especially when they are deep, capturing noise in the training data rather than the underlying patterns. By aggregating the predictions from a multitude of trees, the ensemble method averages out these errors and reduces variance, leading to a more generalized model that performs better on unseen data.

Moreover, multiple decision trees can collectively capture a wider range of interactions and relationships in complex datasets than what a single tree might grasp, resulting in more accurate and reliable predictions in applications such as risk modeling. The power of ensembles lies in their ability to learn from the diversity of the individual trees, which compensates for their individual weaknesses and enhances overall model performance.