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The reasoning behind identifying the choice about requiring extensive parameter tuning as not being a characteristic of decision trees stems from how decision trees function in machine learning. Unlike many other algorithms, such as neural networks or support vector machines, decision trees have a relatively straightforward set of parameters. Typically, they require minimal tuning, allowing users to specify the maximum depth of the tree or the minimum samples required to split a node. This simplicity is one of the attractive features of decision trees, making them accessible and easy to implement, especially for beginners.

Decision trees are characterized by their intuitive structure, making them easy to visualize and interpret. They can naturally handle both numerical and categorical data without needing extensive preprocessing, which adds to their versatility. Their potential to overfit the training data is well noted, particularly if the tree is allowed to grow too deep without restriction. This balance between complexity and generalization is a key aspect of using decision trees effectively in practice.