Attributes are also known as features, predictors or independent variables. A decision tree imposes a partitioning in an ordered attribute space that can be geometrically represented as a collection of hyperplanes and regions. For this reason, splits are often referred to as hyperplanes, attributes as dimensions and objects as points. A class label is also referred to as category or dependent variable. Ordered domains are equivalent to or comprise of continuous, integer, real-valued and monotonous domains.
Unordered domains have categorical, discrete or free variables.
Internal nodes are the same as non-terminals or test nodes. Leaf nodes are the terminal nodes or decision nodes. Splits are the same as cuts or tests. Goodness measures are also known as feature evaluation criterion, feature selection criterion, impurity measure or splitting rule. A leaf node is pure when it is homogeneous.