Logic clarification behind Decision Tree

machine_learning
data_science

#1

Hi Guys,
So lets say that i am using Information Gain logic to build the decision tree. Lets say i have 10 independent variables and 11th variable is target variable (with Yes/No).

What i have understood is that at the first step decision tree model will calculate the information gain of all 10 variables and which ever is high will start the decision tree main node using that variable.

Now for next level of sub-nodes in the tree, how does decision tree decides which variable to choose? Does it calculate information gain again for the remaining 9 variables and so on ?

Please clarify and also if you have any link where this is explained in detailed - please share


#2

Hey @karthik_Van,

Check out the following article for a detailed description of tree-based modeling

Regards,
Sanad :slight_smile: