Decision Tree

A decision tree is a supervised machine-learning model that represents a classification or regression decision as a tree of yes/no tests on input features, with predictions at the leaves. Well-known algorithms include ID3, C4.5, CART, and Random Forests. Decision trees are widely used in accessibility and assistive-technology research because their rule-like output is relatively interpretable: a clinician or carer can read the model as a set of if-then statements (for example, 'if bed sensor > 12 and living-room sensor ≤ 10, then the person is likely sleeping'). Compared to opaque models such as deep neural networks, this transparency matters when the inference is feeding decisions about a disabled or older person's care.