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A Unified Approach to Interpreting Model Predictions [article]

Scott Lundberg, Su-In Lee
2017 arXiv   pre-print
To address this problem, we present a unified framework for interpreting predictions, SHAP (SHapley Additive exPlanations). SHAP assigns each feature an importance value for a particular prediction.  ...  However, the highest accuracy for large modern datasets is often achieved by complex models that even experts struggle to interpret, such as ensemble or deep learning models, creating a tension between  ...  We would like to thank Marco Ribeiro, Erik Štrumbelj, Avanti Shrikumar, Yair Zick, the Lee Lab, and the NIPS reviewers for feedback that has significantly improved this work.  ... 
arXiv:1705.07874v2 fatcat:5xxg6yvljrhf5mtssjo6jgohqu

TSInterpret: A unified framework for time series interpretability [article]

Jacqueline Höllig, Cedric Kulbach, Steffen Thoma
2022 arXiv   pre-print
Interpretability approaches and their visualizations are diverse in use without a unified API or framework.  ...  To close this gap, we introduce TSInterpret an easily extensible open-source Python library for interpreting predictions of time series classifiers that combines existing interpretation approaches into  ...  In order to focus the research and application of time series prediction models not only on performance but also on understanding the model in a practical environment, a further development step is to  ... 
arXiv:2208.05280v2 fatcat:ualype26wnfxta3wdbn7ow5xqu

InterpretML: A Unified Framework for Machine Learning Interpretability [article]

Harsha Nori and Samuel Jenkins and Paul Koch and Rich Caruana
2019 arXiv   pre-print
The package enables practitioners to easily compare interpretability algorithms by exposing multiple methods under a unified API, and by having a built-in, extensible visualization platform.  ...  InterpretML also includes the first implementation of the Explainable Boosting Machine, a powerful, interpretable, glassbox model that can be as accurate as many blackbox models.  ...  Acknowledgments We would like to acknowledge everyone on our acknowledgements.md file for their support on this project. We also depend on many amazing software packages and research: scikit-learn  ... 
arXiv:1909.09223v1 fatcat:uapsreh465cftf4u25rbamxxje

Unification by Fiat: Arrested Development of Predictive Processing

Piotr Litwin, Marcin Miłkowski
2020 Cognitive Science  
Predictive processing (PP) has been repeatedly presented as a unificatory account of perception, action, and cognition.  ...  Otherwise, PP will ultimately fail as a unified theory of cognition.  ...  We would also like to express our gratitude to the Reviewers and the Editor for their in-depth comments and suggestions which led to a major improvement of the paper.  ... 
doi:10.1111/cogs.12867 pmid:32594580 fatcat:sx6cjskzozcohi4zjghhh3naei

ferret: a Framework for Benchmarking Explainers on Transformers [article]

Giuseppe Attanasio, Eliana Pastor, Chiara Di Bonaventura, Debora Nozza
2022 arXiv   pre-print
It offers a unified benchmarking suite to test and compare a wide range of state-of-the-art explainers on any text or interpretability corpora.  ...  We introduce ferret, an easy-to-use, extensible Python library to explain Transformer-based models integrated with the Hugging Face Hub.  ...  Toolkits for post-hoc interpretability offer built-in methods to explain model prediction, typically through a code interface. ferret builds on and extends this idea to a unified framework to generate  ... 
arXiv:2208.01575v1 fatcat:bhflieivkfetdij2rwnhx57aiu

DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural Networks [article]

Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen
2018 arXiv   pre-print
Furthermore, separate and joint attention mechanisms are developed and embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target  ...  Lastly, alternative representations using protein sequences or compound graphs and a unified RNN/GCNN-CNN model using graph CNN (GCNN) are also explored to reveal algorithmic challenges ahead.  ...  Agency (FA8750-18-2-0027 to ZW).  ... 
arXiv:1806.07537v2 fatcat:lauhhpn7fbextfkoiyflmltv2e

Modeling creative abduction Bayesian style

Christian J. Feldbacher-Escamilla, Alexander Gebharter
2018 European Journal for Philosophy of Science  
In this paper we take up the idea of combining creative abduction with causal principles and model instances of successful creative abduction within a Bayes net framework.  ...  In particular, we discuss use-novel predictions, confirmation, and the problem of underdetermination in the context of abductive inferences.  ...  the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s13194-018-0234-4 pmid:30873247 pmcid:PMC6383602 fatcat:wn4ftktqina7jd6gipzjbgsoyy

An objective prior that unifies objective Bayes and information-based inference [article]

Colin H. LaMont, Paul A. Wiggins
2015 arXiv   pre-print
The w-prior is chosen to make the marginal probability an unbiased estimator of the predictive performance of the model. This definition has several other natural interpretations.  ...  We expect this new objective-Bayes approach to inference to be widely-applicable to machine-learning problems including singular models.  ...  Finally, we discuss predictivity as the unifying principle that is common to all three principle paradigms of inference. Preliminaries.  ... 
arXiv:1506.00745v2 fatcat:isfont2ozfcuhd4dueg7ikkm54

Feature Removal Is a Unifying Principle for Model Explanation Methods [article]

Ian Covert, Scott Lundberg, Su-In Lee
2020 arXiv   pre-print
Researchers have proposed a wide variety of model explanation approaches, but it remains unclear how most methods are related or when one method is preferable to another.  ...  Our framework unifies 25 existing methods, including several of the most widely used approaches (SHAP, LIME, Meaningful Perturbations, permutation tests).  ...  ML interpretability broadly aims to provide insight into how models make predictions. This is particularly important when f is a complex model, such as a neural network or a decision forest.  ... 
arXiv:2011.03623v1 fatcat:yrvmo5pdgbdfrmnfujg6elqkve

An unexpected unity among methods for interpreting model predictions [article]

Scott Lundberg, Su-In Lee
2016 arXiv   pre-print
Understanding why a model made a certain prediction is crucial in many data science fields.  ...  However, with large modern datasets the best accuracy is often achieved by complex models even experts struggle to interpret, which creates a tension between accuracy and interpretability.  ...  can be extended to unify and justify a wide variety of recent approaches to interpreting model predictions ( Figure 1 ).  ... 
arXiv:1611.07478v3 fatcat:olnmbpcuwjbaba6zisc6rvb22i

DeepAffinity: Interpretable Deep Learning of Compound Protein Affinity through Unified Recurrent and Convolutional Neural Networks [article]

Mostafa Karimi, Di Wu, Zhangyang Wang, Yang Shen
2018 bioRxiv   pre-print
Furthermore, an attention mechanism is embedded to our model to add to its interpretability, as illustrated in case studies for predicting and explaining selective drug-target interactions.  ...  Under novel representations of structurally-annotated protein sequences, a semi-supervised deep learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both  ...  Agency (FA8750-18-2-0027 to ZW).  ... 
doi:10.1101/351601 fatcat:6hgkfdf3dfhz5lgvbizworxhje

Sequence-level Confidence Classifier for ASR Utterance Accuracy and Application to Acoustic Models [article]

Amber Afshan, Kshitiz Kumar, Jian Wu
2021 arXiv   pre-print
Recently, AM customization has gained traction with the widespread use of unified models.  ...  Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR) systems lack universal interpretation and vary with updates to the underlying confidence or acoustic models (AMs  ...  We customized the unified model to a voice search task.  ... 
arXiv:2107.00099v1 fatcat:ptzavg3uivdgdfsye5tac7gwly

A Unified Process Model of Syntactic and Semantic Error Recovery in Sentence Understanding [article]

Jennifer K. Holbrook, Kurt P. Eiselt, and Kavi Mahesh (College of Computing, Georgia Tech)
1994 arXiv   pre-print
Another way to interpret Stowe's finding is this: the human sentence processor consists of a single unified processing module utilizing multiple independent knowledge sources in parallel.  ...  Our previous work in modeling the sentence processor resulted in a model in which different processing modules used separate knowledge sources but operated in parallel to arrive at the interpretation of  ...  a unified model with a single processor operating on multiple independent sources of knowledge.  ... 
arXiv:cmp-lg/9408021v1 fatcat:n6wdicre5bak7pwm4qtow7fguy

Neural-Symbolic Commonsense Reasoner with Relation Predictors [article]

Farhad Moghimifar, Lizhen Qu, Yue Zhuo, Gholamreza Haffari, Mahsa Baktashmotlagh
2021 arXiv   pre-print
In addition to providing interpretable explanation, the learned logic rules help to generalise prediction to newly introduced events.  ...  However, existing approaches in this area are limited by considering CKGs as a limited set of facts, thus rendering them unfit for reasoning over new unseen situations and events.  ...  Nevertheless, the inference process in existing approaches is like a black box, where internal behaviour of the model is hardly interpretable.  ... 
arXiv:2105.06717v1 fatcat:lki647oflrey3esx6gxrgmtc2a

On the relationship between eye movements and the N400 in sentence processing: A unifying statistical approach

Phillip M. Alday, Franziska Kretzschmar
2017 Figshare  
Complementing co-registration of EEG and eye movements, advanced statistical modelling may be a viable solution to unify data patterns -and, hence, interpretations -across these online processing methods  ...  Posterior predictive checks suggest a good to excellent fit to the data for all models.  ... 
doi:10.6084/m9.figshare.5267566.v1 fatcat:dfemkoeoanfnnh4bsfmizixgmq
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