What action does not help address the issue of overfitting in a decision tree model?

The action that does not help address the issue of overfitting in a decision tree model is applying bagging in constructing the decision tree model.

Bagging, or Bootstrap Aggregating, is an ensemble technique that aims to improve the stability and accuracy of machine learning algorithms by combining the predictions from multiple models. While bagging can reduce overfitting in certain models by averaging numerous predictions, in the context of decision trees specifically, it integrates multiple trees to form a single composite prediction. This approach often leads to a decrease in variance and can effectively counterbalance overfitting for decision trees.

On the other hand, applying cost complexity pruning, increasing the minimum number of observations required in terminal nodes, and decreasing the number of splits directly modify the structure of the decision tree to reduce its complexity. These strategies focus on preventing the model from becoming too complex, thereby controlling overfitting. Therefore, while bagging is a valuable technique, it does not specifically target overfitting at the level of an individual decision tree model in the way that the other options do.