Classification and regression trees

Partitions (left) and decision tree structure (right) for a classification tree model with three classes labeled 1, 2, and 3. At each intermediate node, a case goes to the left child node if and only if the condition is satisfied. The predicted class is given beneath each leaf node.

Observed versus predicted values for the tree models in Figure 5 and two ordinary least squares models.

Data and fitted regression functions in the three leaf nodes of the GUIDE piecewise simple quadratic model in Figure 5(c).

Data and fitted regression lines in the five leaf nodes of the GUIDE piecewise simple linear model in Figure 5(b).

GUIDE piecewise constant, simple linear, simple quadratic, and stepwise linear, and M5' piecewise constant regression trees for predicting FEV. The RPART tree is a subtree of (a), with leaf nodes marked by asterisks (*). The mean FEV and linear predictors (with signs of the coefficients) are printed beneath each leaf node. Variable ht2 is the square of ht.

CRUISE, C4.5, and GUIDE trees for car data with manuf included. The CHAID, RPART, and QUEST trees are trivial with no splits. Sets S₁ and S₂ are (Plymouth, Subaru) and (Lexus, Lincoln, Mercedes, Mercury, Volvo), respectively, and S₃ is the complement of S₁ ∪ S₂.

Data and split at the node marked with an asterisk (*) in the GUIDE tree in Figure 2.

CRUISE, QUEST, RPART, C4.5, and GUIDE trees for car data without manuf. The CHAID tree is trivial with no splits. At each intermediate node, a case goes to the left child node if and only if the condition is satisfied. The predicted class is given beneath each leaf node.