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Question & Answer1
Understand Question2
Review Options3
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Explore TopicChoose the Best Answer
A
Predicting house prices based on square footage and number of bedrooms using a linear regression model.
B
Classifying emails as spam or not spam based solely on the presence of certain keywords.
C
Grouping customers into segments based on their purchasing behavior without any defined parameters.
D
Generating random numbers for a simulation without any underlying structure.
Understanding the Answer
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Answer
A parametrized predictor is useful when you want a single model that can be tuned for different inputs or conditions. For example, a machine‑learning model that predicts house prices could take the number of bedrooms, square footage, and location as parameters, letting the same algorithm adjust its weights for each house. In this case the predictor’s formula changes only by plugging in the new parameters, rather than building a new model each time. This scenario shows how a single parametrized predictor can be reused for many similar prediction tasks.
Detailed Explanation
A uses a linear regression model. Other options are incorrect because B uses a fixed list of keywords; C groups customers without using any numbers to predict.
Key Concepts
Parametrized Predictors
Predictive Modeling
Linear Regression
Topic
Parametrized Predictors
Difficulty
easy level question
Cognitive Level
understand
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Question 3In a logistic regression model, which of the following best describes the role of a parametrized predictor?
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