The Decision List node identifies subgroups, or segments,
that show a higher or lower likelihood of a given binary outcome relative to the overall population.
For example, you might look for customers who are unlikely to churn or are most likely to respond
favorably to a campaign. You can incorporate your business knowledge into the model by adding your
own custom segments and previewing alternative models side by side to compare the results. Decision
List models consist of a list of rules in which each rule has a condition and an outcome. Rules are
applied in order, and the first rule that matches determines the outcome.
Decision List models use a single target and one or more input fields. A frequency field can
also be specified. See Common modeling node properties for more information.
model_output_type
ModelInteractiveBuilder
search_direction
UpDown
Relates to finding segments; where Up is the equivalent of High Probability, and Down is the
equivalent of Low Probability.
target_value
string
If not specified, will assume true value for flags.
max_rules
integer
The maximum number of segments excluding the remainder.
min_group_size
integer
Minimum segment size.
min_group_size_pct
number
Minimum segment size as a percentage.
confidence_level
number
Minimum threshold that an input field has to improve the likelihood of response (give lift),
to make it worth adding to a segment definition.
max_segments_per_rule
integer
mode
SimpleExpert
bin_method
EqualWidthEqualCount
bin_count
number
max_models_per_cycle
integer
Search width for lists.
max_rules_per_cycle
integer
Search width for segment rules.
segment_growth
number
include_missing
flag
final_results_only
flag
reuse_fields
flag
Allows attributes (input fields which appear in rules) to be re-used.
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