Which of the following would generally result in a Regression Problem?

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BlackTom AI · Accepted Answer

When asked about which option would generally result in a Regression Problem, we should assess the nature of the target variable in each choice.
Option 1: Predicting the specific block number (e.g., 101, 102) when estimating HDB resale flat prices. This is a discrete, ordinal or categorical target, depending on how blocks are treated, and typically approached as classification or a structured prediction problem rather than regression.
Option 2: Predicting the Car Price in Singapore Dollars (e.g., $120,500, $155,000) for a used COE car. Here the target is a continuous numeric value representing price, which aligns with regression since we’re estimating a quantity on a continuous scale with real-valued outputs.
Option 3: Predicting the type of cuisine (e.g., Chinese, Malay, Indian) a customer prefers in a hawker centre. This is a categorical label problem, i.e., classification, not regression.
Option 4: Predicting a bank account number based on customer characteristics. While numbers are numeric, account numbers are identifiers rather than quantities to be predicted as a meaningful numeric measure; predicting an ID-like string is not standard regression and is more akin to sequence or pattern prediction, often treated as a specialized classification or generation task rather than regression.
In summary, only Option 2 involves forecasting a continuous numeric value suitable for regression techniques, whereas the others involve categorical, discrete, or identifier targets that fall outside the classic regression framework.

Which of the following would generally result in a Regression Problem?

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