For decision trees, we can either compute the information gain and entropy or gini index in deciding the correct attribute which can be the splitting attribute. Can anyone send an worked out example of Gini index
Lets assume we have 3 classes and 80 objects. 19 objects are in class 1, 21 objects in class 2, and 40 objects in class 3 (denoted as (19,21,40) ).
The Gini index would be: 1- [ (19/80)^2 + (21/80)^2 + (40/80)^2] = 0.6247 i.e. costbefore = Gini(19,21,40) = 0.6247
In order to decide where to split, we test all possible splits. For example splitting at 2.0623, which results in a split (16,9,0) and (3,12,40):
After testing x1
Lets assume we have 3 classes and 80 objects. 19 objects are in class 1, 21 objects in class 2, and 40 objects in class 3 (denoted as (19,21,40) ).
The Gini index would be: 1- [ (19/80)^2 + (21/80)^2 + (40/80)^2] = 0.6247 i.e. costbefore = Gini(19,21,40) = 0.6247
In order to decide where to split, we test all possible splits. For example splitting at 2.0623, which results in a split (16,9,0) and (3,12,40):
After testing x1
You may follow these below links....
http://dni-institute.in/blogs/gini-index-work-out-example/
http://stats.stackexchange.com/questions/95839/gini-decrease-and-gini-impurity-of-children-nodes
I understood that in case of gini index,we try to minimize and take that value for splitting and in ginigain ,the one that gives maximum value for splitting,am I right?But when do we use gini index and ginigain?
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