In models using decision trees for predictions, what is a key advantage of using multiple trees?

Using multiple trees in decision tree models primarily enhances predictive accuracy through a technique known as ensemble learning. This approach aggregates the predictions from various individual decision trees to produce a final prediction that leverages the strengths of each tree while minimizing their individual weaknesses.

By combining the outputs of multiple models, the ensemble can capture a wider variety of patterns in the data and reduce overfitting, which often occurs in single decision trees that may become too tailored to the training data. This leads to better generalization when the model is applied to unseen data, ultimately improving the overall accuracy of the predictions made by the model.

Ensembling methods, such as random forests, take advantage of the diversity among the individual trees, which can help in making more robust and reliable predictions compared to relying on a single tree. This improvement in predictive performance is why the use of multiple trees is a significant advantage in these models.