Decision Tree Learning using ID3 Algorithm | Artificial intelligence | Machine Learning

Veröffentlicht am: 01 Mai 2018
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A Decision tree represents a function that takes as input a vector of attribute values and returns a “decision”—a single output value.
The input and output values can be discrete or continuous.
A decision tree reaches its decision by performing a sequence of tests.
There are many specific decision-tree algorithms. Notable ones include:
ID3 (Iterative Dichotomiser 3)
C4.5 (successor of ID3)
CART (Classification And Regression Tree)
CHAID (Chi-squared Automatic Interaction Detector). Performs multi-level splits when computing classification trees.
MARS: extends decision trees to handle numerical data better.
ID3 is one of the most common decision tree algorithm
Dichotomisation means dividing into two completely opposite things.
Algorithm iteratively divides attributes into two groups which are the most dominant attribute and others to construct a tree.
Then, it calculates the Entropy and Information Gains of each attribute. In this way, the most dominant attribute can be founded.
After then, the most dominant one is put on the tree as decision node.
Entropy and Gain scores would be calculated again among the other attributes.
Procedure continues until reaching a decision for that branch.
algorithm steps:
Calculate the entropy of every attribute using the data set S
Entropy(S) = ∑ – p(I) . log2p(I)
Split the set S into subsets using the attribute for which the resulting entropy (after splitting) is minimum (or, equivalently, information gain is maximum)
Gain(S, A) = Entropy(S) – ∑ [ p(S|A) . Entropy(S|A) ]
Make a decision tree node containing that attribute
Recurse on subsets using remaining attributes.

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