a pre-specified minimum number of training observations that cannot be assigned to each leaf nodes with any splitting methods.all leaf nodes are pure with a single class.The tree will stop growing by the following three criteria (Zhang 2016):
The tree grows from the top (root), at each node the algorithm decides the best split cutoff that results to the greatest purity (or homogeneity) in each subpartition. The leaf nodes of the tree are the outcome variable which is used to make predictions. The tree is placed from upside to down, so the root is at the top and leaves indicating the outcome is put at the bottom.Įach decision node corresponds to a single input predictor variable and a split cutoff on that variable. The resulting tree is composed of decision nodes, branches and leaf nodes. The process continues until some predetermined stopping criteria are met. These rules are produced by repeatedly splitting the predictor variables, starting with the variable that has the highest association with the response variable. The following example represents a tree model predicting the species of iris flower based on the length (in cm) and width of sepal and petal. The decision rules generated by the CART predictive model are generally visualized as a binary tree. a categorical variable, for classification trees.a continuous variable, for regression trees.The produced result consists of a set of rules used for predicting the outcome variable, which can be either: This approach is technically called recursive partitioning.
The algorithm of decision tree models works by repeatedly partitioning the data into multiple sub-spaces, so that the outcomes in each final sub-space is as homogeneous as possible.
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