Partitioning data is very important when determining which multiple methods to apply to a given data...
Partitioning data is very important when determining which multiple methods to apply to a given data set. Which is NOT a benefit of partitioning.
It allows us to use more data points to fit each model
it allows us to check model performance on data not used to create the model
it allows the same error metric to be copied across methods
it helps avoid overfitting
Homework Answers
Partitioning data will reduce number data points to be available to fit each model. So, the correct statement (NOT a benefit of partitioning) is,
It allows us to use more data points to fit each model.
All other options are benefits of partitioning.
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Partitioning data is very important when determining which
multiple methods to apply to a given data...
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whats the answer
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