Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
ERM aims to minimize the average loss over a training dataset.
B
ERM guarantees that the model will perform perfectly on unseen data.
C
The choice of loss function is crucial in the ERM framework.
D
ERM can be applied to any predictive model regardless of its complexity.
E
Overfitting can occur if the model is too complex relative to the dataset.
Understanding the Answer
Let's break down why this is correct
Answer
Empirical Risk Minimization is the idea that a learning algorithm should pick the hypothesis that gives the lowest average loss on the training examples, called the empirical risk. The empirical risk is simply the sum (or mean) of the loss values for each training point, so minimizing it means the model fits the data as closely as possible. This principle can lead to overfitting if the hypothesis space is too large, because the model can fit noise in the training set. For example, in linear regression with squared loss, ERM chooses the line that makes the sum of squared differences between the predicted and actual values as small as possible on the training data. Thus, ERM is a concrete, data‑driven way to select a model by minimizing its average error on the observed samples.
Detailed Explanation
ERM looks at all training examples and calculates how wrong the model is on each one. Other options are incorrect because ERM only optimizes training data; ERM works with many models, but if the model is too complex, it can learn noise.
Key Concepts
Empirical Risk Minimization
Loss Functions
Overfitting
Topic
Empirical Risk Minimization
Difficulty
easy level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of Empirical Risk Minimization, how does overfitting relate to the choice of loss function?
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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 3A data scientist is tasked with building a predictive model to forecast sales based on historical data. To ensure the model performs well, they decide to apply empirical risk minimization (ERM). Which of the following actions best represents the application of ERM in this scenario?
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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 scenarios best exemplifies the application of Empirical Risk Minimization in model training?
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6
Question 6In the context of Empirical Risk Minimization, which factor most directly influences the selection of model parameters?
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7
Question 7How does empirical risk minimization (ERM) ensure that a predictive model generalizes well to unseen data?
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8
Question 8Empirical Risk Minimization (ERM) : Finding the best model parameters :: Gradient Descent : ?
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