# Computing Information Gain for Continuous-Valued Attributes in data mining

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

## Next Similar Tutorials

2. Decision Tree Induction and Entropy in data mining – Click Here
5. Computing Information-Gain for Continuous-Valued Attributes in data mining – Click Here
7. Bagging and Bootstrap in Data Mining, Machine Learning – Click Here
8. Evaluation of a classifier by confusion matrix in data mining – Click Here
9. Holdout method for evaluating a classifier in data mining – Click Here