Given a data set X with 3-dimesional categorical samples: Construct a decision t

Question

Given a data set X with 3-dimesional categorical samples: Construct a decision tree using the computation steps given in the C4.5 algorithm. Make a decision tree that computes the logical AND function. How does it compare to the Perceptron solution? Of the following algorithms: a) C4.5 Decision Tree b) K-Nearest Neighbor c) Naive Bayes i) Which are fast in training but slow in classification? ii) Which one produces classification rules? iii) Which one requires discretization of continuous attributes before application? iiii) Which model is the most complex?

Explanation / Answer

Answer

Task 3.

i. Naive Bayes is fast in training but slow in classification.

ii. C4.5 Decision Tree produces classification rules.

iii. k-nearest neighbor needs discretization of continuous attributes before application.

iv. k-nearest neighbor is the most complex model among the given models.

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