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what is nb? https://skope-rules.readthedocs.io/en/latest/skope_rules.html
For example
skope_rules_clf.rules_
[('Pclass <= 2.5 and isFemale > 0.5', (0.9527320854603895, 0.5283115637180831, 6))]
rules_(dict of tuples (rule, precision, recall, nb).) The collection of n_estimators rules used in the predict method.
The rules are generated by fitted sub-estimators (decision trees).
Each rule satisfies recall_min and precision_min conditions.
The selection is done according to OOB precisions.estimators_(list of DecisionTreeClassifier)
The collection of fitted sub-estimators used to generate candidate rules.estimators_samples_(list of arrays)
The subset of drawn samples (i.e., the in-bag samples) for each base estimator.estimators_features_(list of arrays)
The subset of drawn features for each base estimator.max_samples_(integer)
The actual number of samplesn_features_(integer)
The number of features when fit is performed.classes_(array, shape (n_classes,))
The classes labels.
The text was updated successfully, but these errors were encountered:
Nb represents the number of trees had this rule.
Sorry, something went wrong.
nb is the number of times the rule appears (as a tree) in the underlying bagging estimator. the doc is not clear on this, PR welcome to update it.
nb
thanks,the problem is solved
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what is nb?
https://skope-rules.readthedocs.io/en/latest/skope_rules.html
For example
skope_rules_clf.rules_
[('Pclass <= 2.5 and isFemale > 0.5', (0.9527320854603895, 0.5283115637180831, 6))]
rules_(dict of tuples (rule, precision, recall, nb).) The collection of n_estimators rules used in the predict method.
The rules are generated by fitted sub-estimators (decision trees).
Each rule satisfies recall_min and precision_min conditions.
The selection is done according to OOB precisions.estimators_(list of DecisionTreeClassifier)
The collection of fitted sub-estimators used to generate candidate rules.estimators_samples_(list of arrays)
The subset of drawn samples (i.e., the in-bag samples) for each base estimator.estimators_features_(list of arrays)
The subset of drawn features for each base estimator.max_samples_(integer)
The actual number of samplesn_features_(integer)
The number of features when fit is performed.classes_(array, shape (n_classes,))
The classes labels.
The text was updated successfully, but these errors were encountered: