Learning Path
Question & Answer
Choose the Best Answer
By minimizing the loss only on the training data
By finding parameters that minimize the average loss on the training set
By selecting the most complex model available
By maximizing the average accuracy on the training set
Understanding the Answer
Let's break down why this is correct
ERM looks at the average loss over all training examples. Other options are incorrect because Some think only reducing loss on the training set guarantees good performance; Choosing the most complex model sounds powerful, but a very complex model can fit noise instead of real patterns.
Key Concepts
Empirical Risk Minimization
medium level question
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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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