The ID3 algorithm constructs a decision tree depending on the given dataset. The branches and nodes are characterized by specific logical outcomes featured in the dataset. The speaker identifies two important terms: information gain, and entropy. Entropy is derived from Information Theory and is described as the average of the information embedded in each message at the receiver. Informally, entropy is intuitively understood as impurity and the information content is directly proportional to entropy. This means that, higher the entropy, the information content is higher. The change in information entropy from one distinct state to another is termed as information gain. The aim of constructing a decision tree is to find the attribute that returns the highest information gain.
The presenter explains that the ID3 algorithm accepts training data and attributes list as input and returns a decision tree as output. The procedure for the ID3 algorithm may be summarised in the following points. Initially, the entropy is calculated for each attribute in the dataset. The attribute with minimum entropy is used as reference and ...
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... It is commonly utilised by the machine learning community for learning and analysing algorithms and as a source of data sets.
The implementation involves an example of “Whether to play Tennis”. It consists of various factors such as temperature, humidity, and weather. Each attribute is tagged to a row number termed as “rownum”. Based on the combinations of the different factors, a column of “Whether to play Tennis” has a binary option of “Yes” or “No”.
The speaker then concludes the presentation by stating that this project builds a decision tree using the ID3 algorithm and derives a set of rules. The primary focus is for data stored across multiple SQL server databases. It is also worthy to mention the importance of validating the attributes and pruning the decision tree for a complex model. Results may not possess coherence if these factors are not taken care of.
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