Model validation
5 practice questionsData ScienceStep-by-step solutions
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1
K-fold cross-validation improves performance estimates by:
A.Using all data only for training
B.Splitting data into k parts and rotating which part is the validation set
C.Removing the test set
D.Training on the test set
2
The bias-variance tradeoff describes the balance between:
A.Training speed and memory
B.A model's error from oversimplification versus oversensitivity to data
C.Data size and feature count
D.Precision and recall
3
k-fold cross-validation evaluates a model by:
A.Training on the test set
B.Splitting data into k folds and rotating which fold is used for validation
C.Using all data for training only
D.Ignoring the labels
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