A company wants to develop a model that can determine if a transaction is fraudulent or legitimate. What type of machine learning task is appropriate for this scenario?
Classification
Clustering
Dimensionality Reduction
Regression
Classification - This is the correct answer. Fraud detection is a classification task because the model needs to classify each transaction as either fraudulent or legitimate, which involves assigning data points to predefined categories or labels.
Regression is used for predicting continuous numerical values for example sales forecasts or prices, not for classifying transactions into categories such as "fraudulent" or "legitimate."
Clustering is an unsupervised learning technique used to group data based on similarities, but it is not suitable for determining whether a transaction is fraudulent or legitimate, which requires labeled data and a classification approach.
Dimensionality Reduction is used to reduce the number of features in the data, typically for improving performance or visualization, but it is not a task in itself for determining fraud or legitimacy.
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