Why are Decision Trees considered non-parametric models?

Decision Trees are non-parametric because they do not assume a fixed functional form and grow structure based on data.

A parametric model has a fixed number of parameters regardless of dataset size (like linear regression). Decision Trees, however, grow their structure dynamically based on the data distribution. The number of nodes, splits, and leaves increases as needed. This flexibility allows them to model complex relationships without assuming linearity or a predefined equation. However, this also increases the risk of overfitting if not controlled through pruning or depth limits.