Decision Trees

Question 1

what are the decision tree algorithms

Answer 1

*C5.0

*C&R Tree (Classification & Regression Tree)

*CHAID (Chi-squared Automatic Interaction Detection)

*QUEST (Quick, Unbiased and Efficient Statistical Tree)

Question 2

advantages of using decision trees

Answer 2

Strengths
–Able to generate understandable rules.

–Perform classification without requiring much computation.

–Able to handle both continuous and categorical variables.

–Provide a clear indication of which fields are most important for prediction or classification.

Question 3

disadvantages of using decision trees

Answer 3

Weaknesses
–Less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute.

–Prone to errors in classification problems with many class and relatively small number of training examples.

–Can be computationally expensive to train. At each node, each candidate splitting field must be sorted before its best split can be found. In some algorithms, combinations of fields are used and a search must be made for optimal combining weights.