k-fold cross-validation is one of the most popular strategies widely used by data scientists. It is a data partitioning strategy so that you can effectively use your dataset to build a more generalized model.
wherein the K-Fold Cross Validation the dataset is split into a K number of sections/folds where each fold is used as a testing set at some point. the current study took the scenario of 3-Fold cross-validation(K=3). Here, the data set is split into 3 folds. In the first iteration, the first fold is used to test the model and the rest are used to train the model. In the second iteration, the 2nd fold is used as the testing set while the rest serve as the training set. This process is repeated until each fold of the 3 folds have been used as the testing dataset (see Fig 9)
k-fold
cross-validation is one of the most popular strategies
widely
used
by data scientists. It is a data partitioning strategy
so
that you can
effectively
use
your dataset to build a more generalized model.
wherein
the K-Fold Cross Validation the dataset
is split
into a K number of sections/folds where each
fold
is
used
as a testing set at
some
point.
the
current
study took the scenario of
3-Fold
cross-validation(K=3). Here, the data set
is split
into 3
folds
. In the
first
iteration, the
first
fold
is
used
to
test
the model and the rest are
used
to train the model. In the second iteration, the 2nd
fold
is
used
as the testing set while the rest serve as the training set. This process
is repeated
until each
fold
of the 3
folds
have been
used
as the testing dataset (
see
Fig 9)