Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
A complex loss function always leads to overfitting.
B
A simple loss function prevents overfitting by reducing model complexity.
C
Overfitting occurs when the model minimizes the training loss function too much without considering validation loss.
D
A loss function cannot affect the overfitting behavior of a model.
Understanding the Answer
Let's break down why this is correct
Answer
In Empirical Risk Minimization the loss function tells the model how bad each prediction error is, so it shapes what the model tries to minimize. If the loss is very harsh for small mistakes—like a squared‑error loss that squares every error—a model will chase every tiny noise in the training data, leading to overfitting. Conversely, a loss that is flatter or caps large errors—such as a Huber loss—keeps the model from reacting too strongly to outliers and can reduce overfitting. For example, a student training a regression model might see that using mean squared error makes the model fit the training set perfectly but perform poorly on new data, while switching to mean absolute error gives a smoother fit that generalizes better. Thus, the choice of loss function directly influences how aggressively a model learns from training data and whether it overfits.
Detailed Explanation
When a model focuses only on reducing training loss, it can fit noise in the data. Other options are incorrect because The idea that a complex loss function always causes overfitting is a misconception; Thinking that a simple loss function stops overfitting mixes up loss shape with model size.
Key Concepts
Loss function
Overfitting
Topic
Empirical Risk Minimization
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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2
Question 2In the context of Empirical Risk Minimization, which of the following scenarios is most likely to lead to underfitting while impacting the generalization error negatively?
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3
Question 3In the context of Empirical Risk Minimization, how does the choice of a loss function affect the consistency of estimators within a given hypothesis space?
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4
Question 4In the context of Empirical Risk Minimization, the process of selecting parameters that minimize the average loss is often referred to as __________.
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5
Question 5Which of the following statements accurately describe Empirical Risk Minimization (ERM)? Select all that apply.
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6
Question 6Which of the following scenarios best exemplifies the application of Empirical Risk Minimization in model training?
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7
Question 7In the context of Empirical Risk Minimization, which factor most directly influences the selection of model parameters?
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8
Question 8If a predictive model using empirical risk minimization consistently underperforms on unseen data, what might be the underlying cause?
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