Learning Path
Question & Answer
Choose the Best Answer
The chosen loss function
The size of the dataset
The complexity of the model
The random initialization of parameters
Understanding the Answer
Let's break down why this is correct
The loss function tells the algorithm how bad a prediction is. Other options are incorrect because Many think a bigger dataset forces the model to pick different parameters; People often think a more complex model automatically changes parameters.
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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