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Loss functions measure the difference between predicted and actual values.
All loss functions are linear functions.
A common example of a loss function is the quadratic loss function.
Loss functions are only used in regression problems.
Loss functions help in optimizing the performance of predictive models.
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Loss functions compare what the model predicts to the true answer. Other options are incorrect because Many people think every loss function is a straight line; Some believe loss functions belong only to regression.
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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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