The objective of this script is to perform a k-fold cross validation of a model built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds a model with the rest of data
Evaluates the model 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 a model built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds a model with the rest of data
Evaluates the model 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 a model built from a dataset. The algorithm:
Divides the dataset in k parts
Holds out the data in one of the parts and builds a model with the rest of data
Evaluates the model 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.
