The objective of this script is to perform a 5-fold cross validation of the model built from a dataset by using the default choices in all the available configuration parameters. Thus, the only input needed in for the script to run is the name of the dataset used to both train and test de models in the cross validation. The algorithm:
Divides the dataset in 5 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 5 parts, so that 5 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 5 evaluations created in the cross-validation process.
The objective of this script is to perform a 5-fold cross validation of the model built from a dataset by using the default choices in all the available configuration parameters. Thus, the only input needed in for the script to run is the name of the dataset used to both train and test de models in the cross validation. The algorithm:
Divides the dataset in 5 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 5 parts, so that 5 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 5 evaluations created in the cross-validation process.
The objective of this script is to perform a 5-fold cross validation of the model built from a dataset by using the default choices in all the available configuration parameters. Thus, the only input needed in for the script to run is the name of the dataset used to both train and test de models in the cross validation. The algorithm:
Divides the dataset in 5 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 5 parts, so that 5 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 5 evaluations created in the cross-validation process.
