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Question & Answer
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To increase the training dataset size
To estimate the generalization error of a model
To reduce the computational complexity of the learning algorithm
To optimize the feature selection process
Understanding the Answer
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Cross-validation splits the data into parts, trains on some parts, and tests on the rest. Other options are incorrect because Cross-validation does not add new data to the training set; Cross-validation actually requires more training runs, which adds computation time.
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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