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pro vyhledávání: '"Lal, G. Roshan"'
Autor:
Lal, G Roshan, Mithal, Varun
We present an algorithm, NN2Rules, to convert a trained neural network into a rule list. Rule lists are more interpretable since they align better with the way humans make decisions. NN2Rules is a decompositional approach to rule extraction, i.e., it
Externí odkaz:
http://arxiv.org/abs/2207.12271
Tree Ensemble (TE) models, such as Gradient Boosted Trees, often achieve optimal performance on tabular datasets, yet their lack of transparency poses challenges for comprehending their decision logic. This paper introduces TE2Rules (Tree Ensemble to
Externí odkaz:
http://arxiv.org/abs/2206.14359
Decision making in crucial applications such as lending, hiring, and college admissions has witnessed increasing use of algorithmic models and techniques as a result of a confluence of factors such as ubiquitous connectivity, ability to collect, aggr
Externí odkaz:
http://arxiv.org/abs/2007.15270
We present a deep, bidirectional, recurrent framework for cleaning noisy and incomplete motion capture data. It exploits temporal coherence and joint correlations to infer adaptive filters for each joint in each frame. A single model can be trained t
Externí odkaz:
http://arxiv.org/abs/1712.03380
Tree Ensemble (TE) models (e.g. Gradient Boosted Trees and Random Forests) often provide higher prediction performance compared to single decision trees. However, TE models generally lack transparency and interpretability, as humans have difficulty u
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91e3219a8254e874d5b6cd021eb7596c
http://arxiv.org/abs/2206.14359
http://arxiv.org/abs/2206.14359