autoclassifiernode properties

Auto Classifier node icon The Auto Classifier node creates and compares a number of different models for binary outcomes (yes or no, churn or do not churn, and so on), allowing you to choose the best approach for a given analysis. A number of modeling algorithms are supported, making it possible to select the methods you want to use, the specific options for each, and the criteria for comparing the results. The node generates a set of models based on the specified options and ranks the best candidates according to the criteria you specify.

Example

node = stream.create("autoclassifier", "My node")
node.setPropertyValue("ranking_measure", "Accuracy")
node.setPropertyValue("ranking_dataset", "Training")
node.setPropertyValue("enable_accuracy_limit", True)
node.setPropertyValue("accuracy_limit", 0.9)
node.setPropertyValue("calculate_variable_importance", True)
node.setPropertyValue("use_costs", True)
node.setPropertyValue("svm", False)

Table 1. autoclassifiernode properties
`autoclassifiernode` Properties	Values	Property description
`target`	field	For flag targets, the Auto Classifier node requires a single target and one or more input fields. Weight and frequency fields can also be specified. See Common modeling node properties for more information.
`ranking_measure`	`Accuracy` `Area_under_curve` `Profit` `Lift` `Num_variables`
`ranking_dataset`	`Training` `Test`
`number_of_models`	integer	Number of models to include in the model nugget. Specify an integer between 1 and 100.
`calculate_variable_importance`	flag
`enable_accuracy_limit`	flag
`accuracy_limit`	integer	Integer between 0 and 100.
`enable_area_under_curve_limit`	flag
`area_under_curve_limit`	number	Real number between 0.0 and 1.0.
`enable_profit_limit`	flag
`profit_limit`	number	Integer greater than 0.
`enable_lift_limit`	flag
`lift_limit`	number	Real number greater than 1.0.
`enable_number_of_variables_limit`	flag
`number_of_variables_limit`	number	Integer greater than 0.
`use_fixed_cost`	flag
`fixed_cost`	number	Real number greater than 0.0.
`variable_cost`	field
`use_fixed_revenue`	flag
`fixed_revenue`	number	Real number greater than 0.0.
`variable_revenue`	field
`use_fixed_weight`	flag
`fixed_weight`	number	Real number greater than 0.0
`variable_weight`	field
`lift_percentile`	number	Integer between 0 and 100.
`enable_model_build_time_limit`	flag
`model_build_time_limit`	number	Integer set to the number of minutes to limit the time taken to build each individual model.
`enable_stop_after_time_limit`	flag
`stop_after_time_limit`	number	Real number set to the number of hours to limit the overall elapsed time for an auto classifier run.
`enable_stop_after_valid_model_produced`	flag
`use_costs`	flag
`<algorithm>`	flag	Enables or disables the use of a specific algorithm.
`<algorithm>.<property>`	string	Sets a property value for a specific algorithm. See Setting algorithm properties for more information.
`use_cross_validation`	field	Fields added to this list can take either the condition or prediction role in rules that are generated by the model. This is on a rule by rule basis, so a field might be a condition in one rule and a prediction in another.
`number_of_folds`	integer	N fold parameter for cross validation, with range from 3 to 10.
`set_random_seed`	boolean	Setting a random seed allows you to replicate analyses. Specify an integer or click Generate, which will create a pseudo-random integer between 1 and 2147483647, inclusive. By default, analyses are replicated with seed 229176228.
`random_seed`	integer	Random seed
`stop_if_valid_model`	boolean
`filter_individual_model_output`	boolean	Removes from the output all of the additional fields generated by the individual models that feed into the Ensemble node. Select this option if you're interested only in the combined score from all of the input models. Ensure that this option is deselected if, for example, you want to use an Analysis node or Evaluation node to compare the accuracy of the combined score with that of each of the individual input models
`set_ensemble_method`	`"Voting" "ConfidenceWeightedVoting" "HighestConfidence"`	Ensemble method for set targets.
`set_voting_tie_selection`	`"Random" "HighestConfidence"`	If voting is tied, select value randomly or by using highest confidence.
`flag_ensemble_method`	`"Voting" "ConfidenceWeightedVoting" "RawPropensityWeightedVoting" "HighestConfidence" "AverageRawPropensity"`	Ensemble method for flag targets.
`flag_voting_tie_selection`	`"Random" "HighestConfidence" "RawPropensity"`	If voting is tied, select the value randomly, with highest confidence, or with raw propensity.