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Question & Answer
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Increasing sample size always decreases both bias and variance.
Increasing sample size primarily reduces variance but may increase bias.
Increasing sample size primarily reduces bias but may increase variance.
Increasing sample size reduces generalization error by primarily decreasing variance.
Understanding the Answer
Let's break down why this is correct
When you collect more data, the model sees more examples of the real world. Other options are incorrect because People think more data always fixes everything, but bias is about how the model is built, not how many examples you have; The idea that more data can raise bias is wrong.
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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