Cross validation (nodes or CSV files)

Steps

1. Run Decision Tree procedure - map two sets of nodes or CSV file paths for training and testing respectively

Cross-Validation from nodes.

Cross-validation from nodes for InfoGain.

RETURN main.cvIG("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for InfoGain. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.

Cross-validation from nodes for GainRatio.

RETURN main.cvGR("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for GainRatio. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.

Cross-validation from nodes for Gini index.

RETURN main.cvGI("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for GiniIndex. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.

Cross-validation from CSV files.

Cross-validation time for data from CSV file for Info Gain.

RETURN main.cvIGCsv("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for InfoGain. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.

Cross-validation time for data from CSV file for GainRatio.

RETURN main.cvGRCsv("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for GainRatio. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.

Cross-validation time for data from CSV file for Gini Index.

RETURN main.cvGICsv("class_label","number_of_folds")

This procedure is used to display cross-validation time for data from the graph database for GiniIndex. "class_label" is the target attribute of the dataset. "number_of_folds" defines the number of folds for the cross-validation.