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Loss Functions
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In the context of machine learning, how does cross-entropy loss serve as an effective loss function for model evaluation metrics, particularly in classification tasks?

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A

It measures the distance between predicted and true distributions

B

It is used primarily for regression tasks

C

It provides a fixed error rate irrespective of class probabilities

D

It is only applicable for binary classification problems

Understanding the Answer

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Cross‑entropy compares the predicted probability distribution to the true distribution. Other options are incorrect because Some think cross‑entropy is used for regression because it uses numbers; Some think it gives a single error rate no matter the probabilities.

Key Concepts

cross-entropy loss
model evaluation metrics
applications in machine learning
Topic

Loss Functions

Difficulty

hard level question

Cognitive Level

understand

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Definition
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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