MC 8-8 (LO 8-2) Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of...

Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of ________
blank
.

Question

MC 8-8 (LO 8-2) Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of...

Predicting which companies are likely to be fraudulent/not fraudulent or likely to be bankrupt or not bankrupt are examples of ________
blank
.

BlackTom AI · Accepted Answer

The question centers on distinguishing companies as fraudulent/not fraudulent or bankrupt/not bankrupt, which involves assigning each company into distinct categories based on observed features.
Option 1: 'regression' — This would require predicting a numeric value (e.g., amount or score). Since the task involves labeling as fraudulent or not, which are categories, regression is not appropriate here.
Option 2: 'time series analysis' — Time series focuses on data indexed in time and forecasting future values in a sequence. While bankruptcy or fraud risk can be tracked over time, the core task described is categorical assignment, not sequence forecasting, so this is not the best fit.
Option 3: 'prediction' — This is a broad term that covers forecasting outcomes but does not specify the type of outcome. Because the scenario involves discrete categories (fraud/not fraudulent, bankrupt/not bankrupt), a more precise term is preferable.
Option 4: 'classification' — This option directly corresponds to assigning items into discrete categories based on features, which matches predicting whether a company belongs to fraud/not fraud and bankrupt/not bankrupt. It captures the categorical nature of the labels involved.

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