The two main downsides to decision trees are that they often don't work well with large datasets, and they are highly susceptible to model overfitting. When tackling a binary classification problem, ...

Decision trees are useful for relatively small datasets that have a relatively simple underlying structure, and when the trained model must be easily interpretable, explains Dr. James McCaffrey of ...

Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...