CODEWITHSUNDEEP
rmachine-learningstatisticsclassificationdecision-tree
user4310282
user4310282

Reputation:

decision tree for significant variables

how can I use decision tree graph to determine the significant variables,I know which one has largest information gain should be in the root of tree which means has small entropy so this is my graph if I want to know which variables are significant how can I interpret

enter image description here

Upvotes: 1

Views: 901

Answers (1)

DAV
DAV

Reputation: 756

What does significant mean to you? At each node, the variable selected it the most significant given the context and assuming that selecting by information gain will actually work (it's not always the case). For example, at node 11, BB is the most significant discriminator given AA>20.

Clearly, AA and BB are the most useful assuming selecting by information gain gives the best way to partition the data. The rest give further refinement. C and N would be next.

What you should be asking is: Should I keep all the nodes?

The answer depends on many things and there is likely no best answer. One way would be by using the total case count of each leaf and merge them.

Not sure how I would do this given your image. It's not really clear what is being shown at the leaves and what 'n' is. Also not sure what 'p' is.

Upvotes: 3

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