## Abstract

Rules embody a set of if-then statements which include one or more conditions to classify a subset

of samples in a dataset. In various applications such classification rules are considered to be interpretable by

the decision makers. We introduce two new algorithms for interpretability and learning. Both algorithms take

advantage of linear programming, and hence, they are scalable to large data sets. The first algorithm extracts

rules for interpretation of trained models that are based on tree/rule ensembles. The second algorithm generates

a set of classification rules through a column generation approach. The proposed algorithms return a set of

rules along with their optimal weights indicating the importance of each rule for classification. Moreover, our

algorithms allow assigning cost coefficients, which could relate to different attributes of the rules, such as; rule

lengths, estimator weights, number of false negatives, and so on. Thus, the decision makers can adjust these

coefficients to divert the training process and obtain a set of rules that are more appealing for their needs. We

have tested the performances of both algorithms on a collection of datasets and presented a case study to elaborate

on optimal rule weights. Our results show that a good compromise between interpretability and accuracy can be

obtained by the proposed algorithms.

of samples in a dataset. In various applications such classification rules are considered to be interpretable by

the decision makers. We introduce two new algorithms for interpretability and learning. Both algorithms take

advantage of linear programming, and hence, they are scalable to large data sets. The first algorithm extracts

rules for interpretation of trained models that are based on tree/rule ensembles. The second algorithm generates

a set of classification rules through a column generation approach. The proposed algorithms return a set of

rules along with their optimal weights indicating the importance of each rule for classification. Moreover, our

algorithms allow assigning cost coefficients, which could relate to different attributes of the rules, such as; rule

lengths, estimator weights, number of false negatives, and so on. Thus, the decision makers can adjust these

coefficients to divert the training process and obtain a set of rules that are more appealing for their needs. We

have tested the performances of both algorithms on a collection of datasets and presented a case study to elaborate

on optimal rule weights. Our results show that a good compromise between interpretability and accuracy can be

obtained by the proposed algorithms.

Original language | English |
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Publisher | arXiv |

Publication status | Published - 21 Apr 2021 |

### Publication series

Series | arXiv preprint arXiv:2104.10751 |
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