When working with decision trees, what is achieved by increasing the minimum number of observations in the terminal nodes?

Increasing the minimum number of observations in the terminal nodes of a decision tree primarily enhances interpretability. This adjustment simplifies the model by ensuring that each terminal node, or leaf, represents a more robust segment of the data, as it requires more data points to form a decision. When terminal nodes have more observations, the rules that guide decisions become based on broader trends rather than anomalies or noise present in smaller samples.

This greater reliance on more substantial data segments makes it easier for stakeholders to understand and trust the reasoning behind the decisions drawn from the model. When the decision thresholds are based on larger groups, it also minimizes the likelihood of capturing random fluctuations in the data, leading to a clearer and more reliable representation of patterns.

While this change can impact other aspects of the model, such as predictive accuracy or complexity, the primary gain from increasing observations in terminal nodes is the enhanced clarity and trustworthiness in the interpretations derived from the model.