The objective of this script is to perform a k-fold cross validation of an
ensemble built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds an ensemble
with the rest of data
Evaluates the ensemble with the hold out data
The second and third steps are repeated with each of the k parts, so that
k evaluations are generated
Finally, the evaluation metrics are averaged to provide the cross-validation
metrics.
The output of the script will be an evaluation ID. This evaluation is a
cross-validation, meaning that its metrics are averages of the k evaluations
created in the cross-validation process.
The objective of this script is to perform a k-fold cross validation of an
ensemble built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds an ensemble
with the rest of data
Evaluates the ensemble with the hold out data
The second and third steps are repeated with each of the k parts, so that
k evaluations are generated
Finally, the evaluation metrics are averaged to provide the cross-validation
metrics.
The output of the script will be an evaluation ID. This evaluation is a
cross-validation, meaning that its metrics are averages of the k evaluations
created in the cross-validation process.
The objective of this script is to perform a k-fold cross validation of an
ensemble built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds an ensemble
with the rest of data
Evaluates the ensemble with the hold out data
The second and third steps are repeated with each of the k parts, so that
k evaluations are generated
Finally, the evaluation metrics are averaged to provide the cross-validation
metrics.
The output of the script will be an evaluation ID. This evaluation is a
cross-validation, meaning that its metrics are averages of the k evaluations
created in the cross-validation process.
