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Concept
Generalization Error
Generalization error
is the measure of how accurately a
machine learning model
performs on
unseen data
compared to its
performance on training data
. It reflects the model's
ability to generalize
from the
training dataset
to new, real-world data, and minimizing this error is crucial for developing
robust predictive models
.
Relevant Fields:
Computer Science and Data Processing 50%
Probability and Statistics 30%
Computational Mathematics 20%
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