Subgroups¶
Early experimental interface to subgroup discovery methods.
Overview¶
Fits rules with conjunctive query based on multiplicative combination of query coverage and effect of query satisfaction on target mean. |
Details¶
-
class
realkd.subgroups.ImpactRuleEstimator(gamma=1.0, search='greedy', search_params={}, verbose=False)¶ Fits rules with conjunctive query based on multiplicative combination of query coverage and effect of query satisfaction on target mean. Formally, for dataset D and target variable y:
\[\mathrm{imp}(q) = (|\mathrm{ext}(q)|/|D|) (\mathrm{mean}(y; \mathrm{ext}(q)) - \mathrm{mean}(y; D)) .\]>>> import pandas as pd >>> titanic = pd.read_csv("../datasets/titanic/train.csv") >>> survived = titanic['Survived'] >>> titanic.drop(columns=['Survived', 'PassengerId', 'Name', 'Ticket', 'Cabin'], inplace=True) >>> subgroup = ImpactRuleEstimator(search='exhaustive', verbose=False) >>> subgroup.fit(titanic, survived) ImpactRuleEstimator(search='exhaustive') >>> subgroup.rule_ +0.7420 if Sex==female >>> subgroup.score(titanic, survived) 0.1262342844834427