Confusion matrix (from nodes or CSV files without tree visualization)

Steps of creating Confusion matrix from nodes.

1. Query the nodes data in Neo4j
2. Run the confusion matrix procedure.

Confusion matrix of decision tree based on Information Gain (without tree visualization) using Neo4j nodes.

RETURN main.confmIG("targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Information Gain from Neo4j nodes.

Confusion matrix of decision tree based on Gini Index (without tree visualization) using Neo4j nodes.

RETURN main.confmGI("targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Gini Index from Neo4j nodes.

Confusion matrix of decision tree based on Information Gain (without tree visualization) using Neo4j nodes.

RETURN main.confmIG("targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Information Gain from Neo4j nodes.

Steps of creating Confusion matrix from CSV files

1. Run confusion matrix procedure - map two sets of CSV file paths for training and testing respectively

Confusion matrix of the decision tree based on Gain Ratio (without tree visualization) using CSV file.

RETURN main.confmGRcsv("trainPath","testPath","targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Gain Ratio from CSV file paths.

Confusion matrix of decision tree based on Gini Index (without tree visualization) using CSV file.

RETURN main.confmGIcsv("trainPath","testPath","targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Gini Index from CSV file paths.

Confusion matrix of decision tree based on Information Gain (without tree visualization) using CSV file.

RETURN main.confmIGcsv("trainPath","testPath","targetAttribute")

This procedure retrieves the confusion matrix of the decision tree based on Information Gain from CSV file paths.