2. Consider the training examples shown in Table 3.5 (please find the table from the Attached screenshot) for a binary classification problem.
(a) Compute the Gini index for the overall collection of training examples.
(b) Compute the Gini index for the Customer ID attribute.
(c) Compute the Gini index for the Gender attribute.
(d) Compute the Gini index for the Car Type attribute using multiway split.
3. Consider the data set shown in Table 4.9 (please find the table in the attached screenshot).
(a) Estimate the conditional probabilities for P(A|+), P(B|+), P(C|+), P(A|-), P(B|-), and P(C|-).
(b) Use the estimate of conditional probabilities given in the previous question to predict the class label for a test sample (A = 0, B = 1, C = 0) using the naıve Bayes approach.
(c) Estimate the conditional probabilities using the m-estimate approach, with p = 1/2 and m = 4.
The post Compute the Gini index for the Car Type attribute appeared first on Homework Aider.
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