Characteristics of ID3 Algorithm & Advantages and Disadvantages of ID3 Algorithm

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 Characteristics of ID3 Algorithm

Major Characteristics of the ID3 Algorithm are listed below:

  • ID3 uses a greedy approach that's why it does not guarantee an optimal solution; it can get stuck in local optimums.
  • ID3 can overfit the training data (to avoid overfitting, smaller decision trees should be preferred over larger ones).
  • This algorithm usually produces small trees, but it does not always produce the smallest possible tree.
  • ID3 is harder to use on continuous data (if the values of any given attribute is continuous, then there are many more places to split the data on this attribute, and searching for the best value to split by can be time-consuming).

Advantages and Disadvantages of ID3 Algorithm

Advantages

  • Inexpensive to construct
  • Extremely fast at classifying unknown records Easy to interpret for small-sized trees.
  • Robust to noise (especially when methods to avoid over-fitting are employed)
  • Can easily handle redundant or irrelevant attributes (unless the attributes are interacting)

Disadvantages

  • The space of possible decision trees is exponentially large. Greedy approaches are often unable to find the best tree.
  • Does not take into account interactions between attributes
  • Each decision boundary involves only a single attribute



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