Final answer:
Trees, or decision trees, offer numerous advantages over Generalized Linear Models (GLMs) including the ability to model non-linear relationships, handle non-parametric data without distribution assumptions, and provide easy interpretation and visualization. They also perform innate feature selection and are robust to outliers, capturing interactions between variables automatically.
Step-by-step explanation:
The benefits of trees compared to Generalized Linear Models (GLMs) pertain to the field of statistics and data analytics. Trees, or decision trees, are a class of non-linear predictive models, which can capture more complex relationships than GLMs, which are linear by nature. Another significant benefit of trees is their ability to handle non-parametric data effortlessly; they don't require assumptions about the distribution of the data as GLMs do.
Decision trees are also easier to interpret and visualize, making them invaluable for communicating results to non-technical stakeholders. They inherently perform feature selection, which can be advantageous when dealing with datasets having numerous variables. Lastly, trees are less affected by outliers and are capable of modeling interactions between variables without explicit specification, unlike GLMs that often require interaction terms to be manually included.