Which of the following is NOT a benefit of using decision trees?

Using decision trees indeed has several notable advantages, and one of the primary benefits is that they can handle both numerical and categorical data seamlessly. This versatility allows decision trees to apply to a wide range of datasets with varying data types, making them a popular choice for various analytical tasks.

Additionally, decision trees are known for being easy to interpret and understand. Their visual nature allows users to see the tree structure, making it intuitive to follow the decision-making process used to arrive at a particular outcome. This interpretability is especially advantageous in contexts where users need to explain the model's reasoning to stakeholders or non-technical audiences.

Another significant benefit of decision trees is their ability to perform automatic feature selection. This means that as the tree grows and splits the dataset, it inherently identifies the most important variables that contribute to decision-making, thereby simplifying the modeling process and potentially improving the model's effectiveness by focusing on relevant features.

The option regarding requiring a lot of data to train effectively is not typically seen as a benefit but rather a limitation of decision trees, as they can be prone to overfitting, especially with small datasets. Thus, having a larger dataset can enhance their performance, but it is not an inherent advantage of their structure.