Which statement about bagging in decision trees is true?

The statement regarding out-of-bag observations being key for addressing overfitting is accurate because out-of-bag (OOB) observations play a crucial role in evaluating the performance of the bagging ensemble. When you create multiple bootstrap samples of the training data, each individual tree is trained on a different subset of the data. The data points that are not included in a particular bootstrap sample are referred to as out-of-bag observations.

These OOB observations can be used to assess the predictive accuracy of the bagging model without needing a separate validation set. By averaging the predictions from multiple trees while utilizing out-of-bag data, bagging can effectively reduce variance and prevent overfitting, as it relies on the collective judgment of many models. This method provides a more generalized performance estimate that can lead to improved robustness of the model.

The other statements do not hold the same validity. The first option about bagging primarily reducing bias is misleading because bagging is primarily designed to reduce variance by stabilizing predictions from multiple individual models. The second option suggests that simply adding more trees guarantees perfect accuracy; however, this is not true since more trees will only improve performance to a certain extent, beyond which diminishing returns occur. Lastly, the fourth option pos