**Cons of using a decision tree:**


Decision trees are frequently used in WSNs because they describe relationships between attributes and classes in a clear and understandable way. A decision tree's paths are made up of a series of conditions that each describes a class. Such decision tree paths can be used to develop rules that can be employed in a WSN to distinguish between different outcomes or phenomena based on measurements taken from sensed data. A decision tree's clarity provides important insights because of the open model learning approach it employs. They are also used because of their simplicity and interpretability in their regulations that can be easily derived from the shape of the tree. It also identifies hyperlink reliability, which is very successful but this set of rules works best with linearly separable records only.
