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A Unified Approach to Interpreting Model Predictions
[article]
2017
arXiv
pre-print
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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]
2022
arXiv
pre-print
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2022 pre-print arXiv:2208.05280v1 fatcat:65vvyymcojdeznxsskcfd3vroa
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]
2019
arXiv
pre-print
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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
2020
Cognitive Science
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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]
2022
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2018
European Journal for Philosophy of Science
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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]
2015
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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]
2018
bioRxiv
pre-print
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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]
2021
arXiv
pre-print
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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]
1994
arXiv
pre-print
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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]
2021
arXiv
pre-print
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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
2017
Figshare
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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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