Learning Path
Question & Answer
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Mean Squared Error Loss
Hinge Loss
Lasso Loss
Cross-Entropy Loss
Understanding the Answer
Let's break down why this is correct
Lasso Loss adds an L1 regularization term to the usual loss. Other options are incorrect because Mean Squared Error Loss only measures the average squared difference between predictions and true values; Hinge Loss is used for support vector machines and focuses on the margin between classes.
Key Concepts
Loss Functions
medium level question
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Deep Dive: 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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