Learning Path
Question & Answer
Choose the Best Answer
Minimizing the loss function
Calculating the average loss
Selecting the training dataset
Evaluating model performance
Understanding the Answer
Let's break down why this is correct
ERM chooses parameters by reducing the average loss. Other options are incorrect because The mistake is thinking Gradient Descent only calculates an average; Some think Gradient Descent picks the training data.
Key Concepts
Empirical Risk Minimization
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Deep Dive: Empirical Risk Minimization
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Definition
Empirical risk minimization (ERM) is a method for selecting the best parameters for a predictive model by minimizing the average loss over a given dataset. ERM aims to find the parameters that provide the best fit to the training data based on a chosen loss function.
Topic Definition
Empirical risk minimization (ERM) is a method for selecting the best parameters for a predictive model by minimizing the average loss over a given dataset. ERM aims to find the parameters that provide the best fit to the training data based on a chosen loss function.
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