discriminantnode properties

Discriminant node icon Discriminant analysis makes more stringent assumptions than logistic regression, but can be a valuable alternative or supplement to a logistic regression analysis when those assumptions are met.

Example

node = stream.create("discriminant", "My node")
node.setPropertyValue("target", "custcat")
node.setPropertyValue("use_partitioned_data", False)
node.setPropertyValue("method", "Stepwise")

Table 1. discriminantnode properties
`discriminantnode` Properties	Values	Property description
`target`	field	Discriminant models require a single target field and one or more input fields. Weight and frequency fields aren't used. See Common modeling node properties for more information.
`method`	`Enter` `Stepwise`
`mode`	`Simple` `Expert`
`prior_probabilities`	`AllEqual` `ComputeFromSizes`
`covariance_matrix`	`WithinGroups` `SeparateGroups`
`means`	flag	Statistics options in the node properties under Expert Options.
`univariate_anovas`	flag
`box_m`	flag
`within_group_covariance`	flag
`within_groups_correlation`	flag
`separate_groups_covariance`	flag
`total_covariance`	flag
`fishers`	flag
`unstandardized`	flag
`casewise_results`	flag	Classification options in the node properties under Expert Options.
`limit_to_first`	number	Default value is 10.
`summary_table`	flag
`leave_one_classification`	flag
`separate_groups_covariance`	flag	Matrices option Separate-groups covariance.
`territorial_map`	flag
`combined_groups`	flag	Plot option Combined-groups.
`separate_groups`	flag	Plot option Separate-groups.
`summary_of_steps`	flag
`F_pairwise`	flag
`stepwise_method`	`WilksLambda` `UnexplainedVariance` `MahalanobisDistance` `SmallestF` `RaosV`
`V_to_enter`	number
`criteria`	`UseValue` `UseProbability`
`F_value_entry`	number	Default value is 3.84.
`F_value_removal`	number	Default value is 2.71.
`probability_entry`	number	Default value is 0.05.
`probability_removal`	number	Default value is 0.10.
`calculate_variable_importance`	flag
`calculate_raw_propensities`	flag
`calculate_adjusted_propensities`	flag
`adjusted_propensity_partition`	`Test` `Validation`