Which of the following typically describes the support for the association rule?

Question

Which of the following typically describes the support for the association rule? A) the number of transactions that include all items that are in the antecedent parts of the rule B) the number of transactions that include all items in the antecedent and consequent parts of the rule C) the number of transactions that include half from the antecedent and half from the consequent D) the number of transactions that include all items that are in the consequent parts of the rule The strength of the association rule, known as lift, is calculated as the ratio of the ____. A) sum of the antecedents and the consequents to the antecedents B) antecedents to the consequents C) support to the confidence level D) confidence to expected confidence In classification, which of the following would be considered as a categorical variable of interest for a credit approval decision for a requester? A) age of the requester B) income of the requester C) revolving balance of the requester D) reject or accept credit approval Which of the following formulas calculates the Euclidean distance between x and Y? A) squareroot (x1 - x2)^2 + (x2 - gamma 2)^2 + ... + (x_n - gamma_n)^2 B) squareroot (x1 + gamma 1) + (x2 + gamma 2) + ... + (x_n + gamma_n) C) squareroot (x1 - gamma 1)^2 + (x2 - gamma 2)^2 + ... + (x_n - gamma_n)^2 D) squareroot (x_n - gamma_n)^2 A tree diagram used to illustrate the sequence of nested clusters produced by hierarchical clustering is known as a ____. a. dendrogram b. scatter chart c. decile-wise lift chart d. cumulative lift tree ____refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data. a. Underfitting b. Overfitting c. Oversampling d. Undersampling

Explanation / Answer

1 Question at a time please, according to Chegg forum rules.

36)

B is the correct answer

a. Underfitting - You should I fitted the data points using a higher degree polynomial. Not an answer

b. Overfitting - Correct Answer. You overfit the data on which you built the model, but the model doesnt fit out of sample data.

c. Oversampling - doesn't have anything to do with fitting datapoints into eqn

d. Undersampling - doesn't have anything to do with fitting datapoints into eqn

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