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# Difference between normal decision tree with information gain criterion and W-J48

Hi. Have a good day everyone!

I want to ask a question

What is the difference between

a) normal decision tree with information gain criterion and

b) W-J48?

Im quite confused with the difference.

Why dont we just use the basic decision tree and choose 'Information gain' for the criterion instead of using W-J48?

Is there any guidelines for me to set the suitable values for parameters in W-J48 such as the confidence threshold for pruning and the minimum number of instances per leaf?

I dont know the suitable value that should be set for the parameters.

I want to ask a question

**1st question**What is the difference between

a) normal decision tree with information gain criterion and

b) W-J48?

Im quite confused with the difference.

Why dont we just use the basic decision tree and choose 'Information gain' for the criterion instead of using W-J48?

**2nd question**Is there any guidelines for me to set the suitable values for parameters in W-J48 such as the confidence threshold for pruning and the minimum number of instances per leaf?

I dont know the suitable value that should be set for the parameters.

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## Answers

1,635UnicornFor more details on the W-J48 implementation you should consult the Weka project documentation.

Lindon Ventures

Data Science Consulting from Certified RapidMiner Experts

1,207UnicornI dont see much difference conceptually between these two as they both use same concept. Information gain ratio and J48 both are worked by Quinlan. Actually both works based on Pruning confidence which is denoted as 'C' and minimal leaf size 'M'. You can see both options in both decision trees.

For your question 2, I see that the default values for confidence 'C' is 0.25 and 'M is '2'. If the confidence is lower the tree is pruned more. You need to try different combinations

Thanks,

Varun

Varun

https://www.varunmandalapu.com/

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