Exam 9: Supervised Data Mining: K-Nearest Neighbors and Naãve Bayes

The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What does the accuracy rate indicate?

For a new observation of (0, 0, 0), what is the k-nearest neighbor when k = 1.

An issue with the naïve Bayes classifier is determining rare outcomes because the estimate is 0. To overcome this problem, the algorithm allows a replacement of zero probability with a nonzero value. This technique is called

What is the Euclidean distance between Observation 2 and the origin point of (0, 0, 0)?

A new applicant, age 45, is applying for a loan. Using the table below, what is the estimated probability the loan will be approved? k = 4.

Of the following options, which does not represent the naïve Bayes method?

A new applicant, age 32, is applying for a loan. Using the table below, what is the estimated probability the loan will default using k = 3.

In a decile-wise lift chart, what does the lift value of the leftmost bar imply?

What is the Euclidean distance between Observation 1 and the origin point of (0, 0, 0)?

The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What is the percent of the results that are incorrectly classified?

An R's ROC curve with AUC = 0.9453 is presented below from an analysis on potential increased membership level from current basic members at Costco Wholesale. What does the AUC indicate on the prediction on increased membership enrollment among current base members?

Marta is partitioning her data set into 60% for training and 40% for validation. She is first specifying 'Member' as her target variable. What will she need to program to ensure consistency to fix a random seed?

If the performance measures from the training data are considerably higher than the values from the validation and test data, what could be the issue?

To validate the model on the validation set, Mary calibrates the output of the model to examining all possible outcomes of the prediction (true positive, true negative, false positive, false negative). One way is to use a cutoff value and use functions such as the ifelse () function. These statements are called

Naïve Bayes classifiers are relatively simple, efficient, and assume dependency among predictors.

This chart determines how well the model performs in terms of sensitivity and specificity.

The chart below is a summary of the main results of a test data set representing the population observed purchasing a virtual digital assistant. What does the accuracy rate indicate?

Mark is reviewing a partial summary of results from a test data set on a small health clinic. With an accuracy 75% (100 count), Sensitivity 50%, and Specificity 100%, can Mark correctly predict the true positive rate to identify those with the flu?

Using the following table, what is the estimate of P(Color) = Black and what is the smoothed estimate of P(Color). k = 1.

Using the following table, which k should be used in the subsequent calculations?