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The statement about Decision Trees being built by splitting the training set into distinct nodes is accurate. Decision Trees function by recursively dividing the dataset into subsets based on the value of input features, creating a structure that resembles a tree. Each internal node represents a feature, each branch represents a decision rule, and each leaf node represents an outcome or a class label. This process of splitting is guided by criteria such as Gini impurity or information gain, which help in determining the most informative splits at each node. As a result, this branching mechanism allows Decision Trees to model complex decision boundaries and relationships within the data.

In contrast, the statements that indicate limitations or characteristics of Decision Trees, such as the notion that they can only classify binary outcomes, or that all Decision Trees share the same structure, do not hold true. Decision Trees are versatile and can handle both binary and multi-class classification problems depending on the data and design. Similarly, their structure varies significantly based on the dataset and the feature values used during the training process. Lastly, the claim that Decision Trees are not influenced by the data set size overlooks the fact that larger datasets can lead to more intricate trees due to the increased amount of information available for splitting, which informs better decision-making in the tree