Operator op-decision_tree

Decision Tree fit

Input and parameters

This operator only takes one input of the functional type table.

It also takes 4 inputs from the user :

• Target : the name of the variable we want to predict in the input table
• ID : the name of the rows (or table key) of the input table
• Max Depth : the maximum depth of the tree. The default value of zero means there is no constraint on the depth of the tree.
• Class Balancing : Apply weightbalancing on the items inversely proportional to class frequencies in the input data

Class Balancing is optional, default False: when True: apply a weight balancing on the classes,inversely proportional to class frequencies in the input data, according to the following formula:

weight(label) = total_samples / (nb_classes*count_samples(label))

Outputs

The operator has three outputs :

• TDT : a special format used by TDT viztool to show details about the built model
• Model : a binary dump of the best model found by the procedure, to be used by the Decision Tree Predict operator
• Dot : the visualisation of the best found decision tree in the GraphViz format

Decision Tree Predict

This IKATS operator implements predict algorithm for DecisionTree of scikit-learn

Input and parameters

This operator takes two inputs :

• Model : previously trained in Decision Tree fit step
• Population : of the functional type table (Ex : test output from TrainTestSplit)

It also takes 3 inputs from the user :

• Target : the name of the variable we want to predict in the input table
• ID : the name of the rows (or table key) of the input table
• Table name : output with features and predictions

Outputs

The operator has two outputs :

• Confusion : confusion_matrix as calculated in scikit-learn
• Score : accuracy score (ratio of correctly predicted observations to the total observations)