Using the ID3 algorithm, construct a decision tree for the given dataset: Outlook, Temperature, Humidity, Wind, Play. Rows: Sunny, Hot, High, Weak, No

Question

Using the ID3 algorithm, construct a decision tree for the given dataset: Outlook, Temperature, Humidity, Wind, Play. Rows: Sunny, Hot, High, Weak, No; Sunny, Mild, High, Strong, No; Overcast, Hot, High, Weak, Yes; Rain, Mild, High, Weak, Yes; Rain, Cool, Normal, Weak, Yes; Rain, Cool, Normal, Strong, No; Overcast, Cool, Normal, Strong, Yes; Sunny, Cool, Normal, Weak, Yes; Sunny, Mild, Normal, Weak, Yes; Rain, Mild, Normal, Weak, Yes.

Accepted Answer

## ID3 decision tree

ID3 selects the attribute with the highest information gain, where entropy is $H(S)=-\sum_c p_c\log_2p_c$. For this dataset, `Outlook` is a useful root: `Overcast` rows are all `Yes`; the `Sunny` rows separate by `Humidity`; the `Rain` rows separate by `Wind`.

The tree correctly classifies all listed training rows. ID3 is greedy: it chooses the best immediate information gain at each step and can overfit without pruning or suitable stopping rules.

ID3 decision tree

Similar questions

Coursify Android App