Decision Tree Induction and Entropy in data mining

Decision Tree Induction

A decision tree is a tree-like structure and consists of following parts(discussed in Figure 1);

1. Root node:
   • age is the root node
2. Branches:
   • Following are the branches;
     • <20
     • 21…50
     • >50
     • USA
     • PK
     • High
     • Low
3. Leaf node:
   • Following are the leaf nodes;
     • Yes
     • No

 

Entropy:

Entropy is a method to measure uncertainty.

• Entropy can be measured between 0 and 1.
• High entropy represents that data have more variance with each other.
• Low entropy represents that data have less variance with each other.

P = Total yes = 9

N = Total no = 5

Note that to calculate the  log² of a number, we can do the following procedure.

For example;

what is  log²  of 0.642?

Ans: log (0.642) / log (2)

=–9/14 * log²(9/14)  –  5/14 * log_² (5/14)

=-9/14 * log²(0.642)  –  5/14 * log_² (0.357)

=-9/14 * (0.639)  –  5/14 * (-1.485)

=0.941

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For Age:

 

Note: if yes =2 and No=3 then entropy is 0.970 and it is same  0.970 if yes=3 and No=2

So here when we calculate the entropy for age<20, then there is no need to calculate the entropy for age >50 because the total number of Yes and No is same.

 

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Note that

• if yes and no are in the following sequence like (0, any number) or (any number, 0) then entropy is always 0.
• If yes and no are occurring in such a sequence (3,5) and (5, 3) then both have the same entropy.
• Entropy calculates the impurity or uncertainty of data.
• If the coin is fair (1/2, head and tail have equal probability, represent maximum uncertainty because it is difficult to guess that head occurs or tails occur) and suppose coin has the head on both sides then the probability is 1/1, and uncertainty or entropy is less.
• if p is equal to q then more uncertainty
• if p is not equal to q then less uncertainty

Now again calculate entropy for;

1. Income
2. Region
3. Credit

For Income:

For Region:

For Credit Rating:

 

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Similarly, you can calculate for all.

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