# 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

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