Methods for knowledge discovery from data and interpretable machine learning. Currently, package contains primarily rule ensembles learners.
>>> import pandas as pd
>>> from sklearn.metrics import roc_auc_score
>>> from realkd.rules import RuleBoostingEstimator, XGBRuleEstimator
>>> titanic = pd.read_csv('../datasets/titanic/train.csv')
>>> survived = titanic.Survived
>>> titanic.drop(columns=['PassengerId', 'Name', 'Ticket', 'Cabin', 'Survived'], inplace=True)
>>> re = RuleBoostingEstimator(base_learner=XGBRuleEstimator(loss=logistic_loss))
>>> re.fit(titanic, survived.replace(0, -1), verbose=0)
-1.4248 if Pclass>=2 & Sex==male
+1.7471 if Pclass<=2 & Sex==female
+2.5598 if Age<=19.0 & Fare>=7.8542 & Parch>=1.0 & Sex==male & SibSp<=1.0
See the full documentation.