Which action does NOT help with interpretability issues in decision trees?

Using more complex algorithms without constraints does not aid in interpretability issues in decision trees. In decision tree modeling, interpretability refers to how easily one can understand the decision rules derived from the model. Simpler and more constrained models are typically more interpretable because they provide clearer decision paths and rules that can be easily followed.

Complex algorithms without constraints tend to produce more intricate models with deeper trees and more splits, which can obscure the decision-making process. This complexity can make it challenging for users to grasp how decisions are made, thereby reducing interpretability.

Conversely, actions like applying cost complexity pruning, increasing terminal node observations, and decreasing the allowed splits focus on simplifying the model or enhancing the clarity of the outcomes, thus improving interpretability. Pruning reduces the size of the tree by removing branches that have little importance, leading to a simpler model. Increasing terminal node observations can provide more robust and reliable predictions in a clearer decision boundary. Reducing the number of splits limits the complexity of the model, thus making it easier to follow the decision process.