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They evaluate how accurately predictions match actual outcomes.
They determine the complexity of a predictive model.
They optimize the computational resources used in prediction.
They define the structure of the predictor function.
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Loss functions give a number that shows how far predictions are from the real values. Other options are incorrect because Some think loss functions decide how complex a model is; Others believe loss functions manage how fast or memory‑efficient a model is.
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Loss Functions
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Definition
Loss functions quantify how well a predictor approximates the true output values. They are used to measure the discrepancy between predicted and actual values. Common examples include quadratic loss functions that penalize the squared differences.
Topic Definition
Loss functions quantify how well a predictor approximates the true output values. They are used to measure the discrepancy between predicted and actual values. Common examples include quadratic loss functions that penalize the squared differences.
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