决策树归纳算法
1980 年，一位名叫 J. Ross Quinlan 的机器研究人员开发了一种称为 ID3（迭代二分法）的决策树算法。后来，他提出了C4.5，它是ID3的继任者。ID3 和 C4.5 采用贪婪的方法。在这个算法中，没有回溯；这些树是以自上而下的递归分而治之的方式构建的。
Generating a decision tree form training tuples of data partition D
Algorithm : Generate_decision_tree
Input:
Data partition, D, which is a set of training tuples
and their associated class labels.
attribute_list, the set of candidate attributes.
Attribute selection method, a procedure to determine the
splitting criterion that best partitions that the data
tuples into individual classes. This criterion includes a
splitting_attribute and either a splitting point or splitting subset.
Output:
A Decision Tree
Method
create a node N;
if tuples in D are all of the same class, C then
return N as leaf node labeled with class C;
if attribute_list is empty then
return N as leaf node with labeled
with majority class in D;|| majority voting
apply attribute_selection_method(D, attribute_list)
to find the best splitting_criterion;
label node N with splitting_criterion;
if splitting_attribute is discrete-valued and
multiway splits allowed then // no restricted to binary trees
attribute_list = splitting attribute; // remove splitting attribute
for each outcome j of splitting criterion
// partition the tuples and grow subtrees for each partition
let Dj be the set of data tuples in D satisfying outcome j; // a partition
if Dj is empty then
attach a leaf labeled with the majority
class in D to node N;
else
attach the node returned by Generate
decision tree(Dj, attribute list) to node N;
end for
return N;
拥有决策树的好处如下 -