Discussion of article "Data Science and Machine Learning (Part 05): Decision Trees"
Hello Mr. Omega,
Thank you so much for the ID3 solution. it is very useful for me. However I provided and attached an excel sheet in this regards, that I think it's clear for your explains.
Many Thanks again,
F.Mahmoudian
Files:
ID3-DecisionTree.zip
39 kb
Fatemeh Haji Mahmoudian #:
Hello Mr. Omega,
Thank you so much for the ID3 solution. it is very useful for me. However I provided and attached an excel sheet in this regards, that I think it's clear for your explains.
Many Thanks again,
F.Mahmoudian
many thanks to it, I'm still trying to figure out how to let the script draw the tree itself
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New article Data Science and Machine Learning (Part 05): Decision Trees has been published:
Decision trees imitate the way humans think to classify data, let's see how to build a tree and use them to classify and predict some data. The main goal of the decision trees algorithm is to separate the data with impurity and into pure or close to nodes.
Decision Trees use multiple algorithms to decide to split a node into two or more subset nodes. The creation of subnodes increases the homogeneity of resultant sub-nodes. In other words, we can say that the purity of the node increases concerning the target variable. The decision tree algorithm splits nodes on all available variables and then selects the split that results in the most homogeneous sub-nodes.
The algorithm selection is based on the type of target variables
The following are the Algorithms used in the Decision Tree
In this article I am going to create a decision tree based on the ID3 algorithm, we'll discuss and use the other algorithms in the next Articles of this series
Author: Omega J Msigwa