In the context of parametrized predictors, which statement best describes the role of parameters in the predictive model?

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Parameters solely determine the output without influencing the model structure.

Parameters shape the model's structure and behavior, affecting predictions.

Parameters are irrelevant if the function g is correctly chosen.

Parameters only serve as constants that must be set prior to training.

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Answer

Parameters are the numbers inside a predictive model that the training process adjusts to make good predictions. They act like knobs that shape how the model turns each input into an output, capturing the patterns that exist in the data. For example, in a linear regression a slope and an intercept are parameters that determine how steeply the predicted value rises as the input grows. By learning the right values for these parameters, the model learns to map new inputs to accurate predictions.

Detailed Explanation

Parameters decide how the model behaves. Other options are incorrect because It assumes parameters only give a number, but they actually control the model’s shape; Even with a good function g, the parameters tell g how to act.

Key Concepts

Parametrized Predictors

Predictive Modeling

Empirical Risk Minimization

Topic

Parametrized Predictors

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medium level question

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understand

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