Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
Mean Squared Error Loss
B
Hinge Loss
C
Lasso Loss
D
Cross-Entropy Loss
Understanding the Answer
Let's break down why this is correct
Answer
The loss function that includes regularization is called a regularized loss, such as an L2‑regularized (ridge) or L1‑regularized (lasso) loss. It adds a penalty term—usually the sum of squared weights or the sum of absolute weights—to the usual error term. This penalty shrinks the weights toward zero, discouraging large coefficients that would fit noise rather than signal. For example, if the error is 0. 5 and the weight vector has a squared norm of 4, an L2 penalty with λ = 0.
Detailed Explanation
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
types of loss functions
regularization in loss functions
Topic
Loss Functions
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1Which of the following statements best describes the relationship between overfitting and underfitting in the context of loss functions?
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Question 2How does Lasso regression modify the loss function to prevent overfitting in predictive models?
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3
Question 3Which of the following statements accurately describe loss functions in machine learning? Select all that apply.
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4
Question 4Which of the following best describes the role of loss functions in predictive modeling?
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Question 5If a machine learning model consistently underperforms on its predictions, which underlying factor is most likely contributing to this issue?
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Question 6How does the implementation of regularization techniques in deep learning models help mitigate overfitting, and what impact does this have on decision-making processes in business applications?
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Question 7In the context of multi-class loss functions, how do precision and recall impact the choice of regularization techniques to prevent overfitting?
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Question 8Which of the following loss functions are suitable for evaluating the performance of multi-class classification models? Select all that apply.
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9
Question 9When selecting a loss function for a multi-class classification task, which factor is most crucial for ensuring model performance?
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Question 10When selecting a loss function for a multi-class classification problem, which of the following considerations is most critical for aligning model performance with classification objectives?
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