Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
To increase the training dataset size
B
To estimate the generalization error of a model
C
To reduce the computational complexity of the learning algorithm
D
To optimize the feature selection process
Understanding the Answer
Let's break down why this is correct
Answer
Cross‑validation is used to estimate how well a model will perform on data it has never seen. It does this by repeatedly training on part of the data and testing on the remainder, which mimics unseen data. This gives a more realistic error estimate and helps prevent overfitting while guiding the choice of hyper‑parameters. For example, with 100 data points, a 5‑fold CV trains on 80 points and tests on 20, repeats five times, and averages the errors to pick the best model.
Detailed Explanation
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
Cross-validation.
Topic
Empirical Risk Minimization
Difficulty
easy level question
Cognitive Level
understand
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