1. Supplemental slides for CSE 327 Prof. Jeff Heflin
Ch. 18 – Learning
Supplemental slides for CSE 327
Prof. Jeff Heflin

2. Decision Tree Learning
function DEC-TREE-LEARN(examples,attribs,default) returns a decision tree if examples is empty then return default else if all examples have the same classification then return the classification else if attribs is empty then return MAJORITY-VALUE(examples) else best  CHOOSE-ATTRIBUTE(attribs,examples) tree  a new decision tree with root test best m  MAJORITY-VALUE(examples) for each value vi of best do examplesi  {elements of examples with best = vi} subtree  DEC-TREE-LEARN(examplesi,attribs – best, m) add a branch to tree with label vi and subtree subtree return true
From Figure 18.5, p. 658

3. Decision Tree Data Set Example Color Size Shape Goal Predicate X1 blue
small
square no X2
green
large
triangle X3
red
circle
yes X4 X5
yellow X6 X7 X8

4. Decision Tree Result Shape? No No Color? Yes No Yes Yes
+: X3,X6 -: X1,X2,X4,X5,X7,X8
Shape?
circle
square
triangle
+: -: X2,X7
+: -: X1,X4,X8
+: X3,X6 -: X5 No No
Color?
green
red
yellow
blue
+: X3,X6 -:
+: -: X5
+: -:
+: -:
Yes No
Yes
Yes