In a decision tree, how does the algorithm pick the attributes for splitting? Would you explain...

In a decision tree, how does the algorithm pick the attributes for splitting?

Would you explain it logically and specifically?

Expert Solution

Answer :

in choice tree :

the calculation id3 is as per the following,

compute the entropy of the objective property
the data set is then spilt on various traits. the entropy for each branch is determined. at that point it is added relatively to get all out entropy for the split. the subsequent entropy is subtracted from the entropy before the split. the outcome is the data increase, or abatement in entropy.
the characteristic is chosen which has the biggest data gain as the choice hub.
a branch with cntropy of 0 is a leaf hub.
a branch with entropy in excess of zero needs further part

the id3 calculation is run recursively on the non leaf branches, until all information is grouped.

A choice tree is constructed best down from a root hub and includes dividing the information into subsets that contain occurrences with comparative qualities (homogeneous). ID3 calculation utilizes entropy to ascertain the homogeneity of an example. On the off chance that the example is totally homogeneous the entropy is zero and on the off chance that the example is similarly separated, at that point it has entropy of one.

orchestra answered 2 years ago

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