A company has historical data that shows whether customers needed long-term support from company staff. The company needs to develop an ML model to predict whether new customers will require long-term

Question

Options

• AAnomaly detection
• BLinear regression
• CLogistic regression
• DSemantic segmentation

• A
  2% (1)
• B
  2% (1)
• C
  95% (41)

Explanation

Logistic regression is the right fit because the task is a binary classification problem - predicting one of two outcomes (will need long-term support vs. won't). Logistic regression outputs a probability between 0 and 1 and maps it to a class label, making it purpose-built for this use case.

• A (Anomaly detection) is wrong because it identifies unusual patterns or outliers, not categorizes outcomes from labeled historical data.
• B (Linear regression) is wrong because it predicts a continuous numerical value (like revenue or temperature), not a discrete category like yes/no.
• D (Semantic segmentation) is wrong because it's a computer vision technique for classifying individual pixels in an image - completely unrelated to tabular customer data.

Memory tip: When a prediction has exactly two outcomes (yes/no, true/false, will/won't), think "logistic = logical choice for binary." The word "logistic" contains the concept of logistics - routing something to one of two destinations.

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