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Concept
K-Fold Cross Validation
K-Fold Cross Validation
is a robust method for assessing the
predictive performance
of a
machine learning model
by
partitioning the dataset
into 'k' subsets, or folds, and iteratively
training and validating the model
'k' times, each time using a different fold as the
validation set
and the
remaining folds
as the training set. This technique helps in
reducing overfitting
and provides a more
generalized evaluation
of the
model's performance
by
averaging the results
across all folds.
Relevant Fields:
Computer Science and Data Processing 70%
Probability and Statistics 30%
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