Computing Information Gain for Continuous-Valued Attributes in data mining

Prof. Fazal Rehman Shamil
Last modified on July 26th, 2020

In this tutorial, we will learn about the computing Information-Gain for Continuous-Valued Attributes.

First of all, lets see that what are continuous attributes?

Continuous attributes can be represented as floating  point variables. For example temperature, width, height, or weight of a body.

To calculate the split point is not a big deal. It is just a just a fun to find the split point. For example, we have the following data mentioned below;

How can we calculate the split point?

Income Class
18 YES
45 NO
18 NO
25 YES
28 YES
28 NO
34 NO

Solution  to calculate the split point

Step 1:

First of all, we need to sort the data in ascending order. After sorting the data, data is shown in the table below.

Income Class
18 YES
18 NO
25 YES
28 YES
28 NO
34 NO
45 NO

Step 2:

Find the midpoint of first two numbers and calculate the information gain

Split point = (18+25) / 2 = 21

  Infoincome<21(D) = 2/7(I(1,1)) + 5/7(I(2,3))

  = 2/7(-1/2(log2(1/2)) – 1/2(log2(1/2))+5/7(-2/5(log2(2/5)) – 3/5(log2(3/5)))

  = 0.98

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