Pseudocode of C5 Algorithm
function C5.0Algorithm(Data, Attributes)
if all examples in Data belong to the same class:
return a leaf node with the class label
else if Attributes is empty:
return a leaf node with the majority class label in Data
else:
Select the best attribute, A, using information gain
Create a decision node for A
for each value v of A:
Create a branch for v
Recursively apply C5.0Algorithm to the subset of Data where A = v
return the decision tree
The C5.0 algorithm for creating a decision tree is described in the pseudocode. The dataset is recursively divided according to the chosen attribute that yields the maximum information gain, until a set of predetermined requirements is satisfied. A leaf node is formed with the class label if every example in the current subset has the same class. In the event that no attributes remain or additional stopping requirements are satisfied, a leaf node bearing the majority class label is generated. If not, the method determines which attribute is the best, builds a decision node, and then iteratively runs the procedure over each subset that the attribute’s values lead to. A decision tree expressing attribute tests as nodes and class labels as leaves is the end product
C5.0 Algorithm of Decision Tree
The C5 algorithm, created by J. Ross Quinlan, is a development of the ID3 decision tree method. By recursively dividing the data according to information gain—a measurement of the entropy reduction achieved by splitting on a certain attribute—it constructs decision trees.
For classification problems, the C5.0 method is a decision tree algorithm. It builds a rule set or a decision tree, which is an improvement over the C4.5 method. The sample is divided according to the field that yields the most information gain for the algorithm to function. Recursively, this method splits each subsample determined by the initial split depending on the field that yields the highest information gain. This process is repeated until a stopping requirement is satisfied.
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