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Methods for knowledge discovery and interpretable machine learning.

Project description

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))
    >>>, 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.

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