{"id":4748,"date":"2025-11-20T01:03:30","date_gmt":"2025-11-20T01:03:30","guid":{"rendered":"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4748"},"modified":"2025-11-20T01:03:30","modified_gmt":"2025-11-20T01:03:30","slug":"explainability-from-vote-traces-in-rf-ensembles-2","status":"publish","type":"post","link":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/?p=4748","title":{"rendered":"Explainability from Vote Traces in RF Ensembles"},"content":{"rendered":"\n<figure class=\"wp-block-embed is-type-wp-embed is-provider-spectrcyde wp-block-embed-spectrcyde\"><div class=\"wp-block-embed__wrapper\">\n<blockquote class=\"wp-embedded-content\" data-secret=\"estSJCoUe5\"><a href=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4744\">Explainability from Vote Traces in RF Ensembles<\/a><\/blockquote><iframe loading=\"lazy\" class=\"wp-embedded-content\" sandbox=\"allow-scripts\" security=\"restricted\" style=\"position: absolute; visibility: hidden;\" title=\"&#8220;Explainability from Vote Traces in RF Ensembles&#8221; &#8212; Spectrcyde\" src=\"https:\/\/172-234-197-23.ip.linodeusercontent.com\/?p=4744&#038;embed=true#?secret=SRtwccLyCm#?secret=estSJCoUe5\" data-secret=\"estSJCoUe5\" width=\"600\" height=\"338\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\"><\/iframe>\n<\/div><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">We convert per-model votes into auditable traces<br>and Shapley-like attributions for RF ensemble decisions. We<br>expose hooks in classify_signal() to log per-model logits, calibrated probabilities, weights, and OSR gates, enabling<br>timeline and contribution analyses with negligible overhead.<br>Our approach provides interpretable explanations for ensemble<br>classifications through vote tracing, model attribution, and disagreement analysis, enhancing trust and debugging capabilities<br>for RF signal classification systems.<br>Index Terms\u2014Explainable AI, ensemble methods, RF signal<br>classification, Shapley values, vote attribution<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/grok.com\/share\/bGVnYWN5_37105d9b-e6a3-45f2-9346-a8d95357f166\">https:\/\/grok.com\/share\/bGVnYWN5_37105d9b-e6a3-45f2-9346-a8d95357f166<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">IX. REPRODUCIBILITY<br>Run the complete pipeline with:<br>DATASET_FUNC=\u201dmy_dataset_module:iter_eval\u201d<br>CLASSIFIER_SPEC=\u201densemble_ml_classifier:EnsembleMLCmake traces &amp;&amp; make figs &amp;&amp; make tables-vt<br>&amp;&amp; make pdf<br>All source code and data generation scripts are included in<br>the repository >>\u00a0<a href=\"https:\/\/github.com\/bgilbert1984\/Vote-Tracing-Model-Level-Explainability-for-RF-Signal-Classification-Ensembles\">bgilbert1984\/Vote-Tracing-Model-Level-Explainability-for-RF-Signal-Classification-Ensembles: Explainable AI, ensemble methods, RF signal classification, exact Shapley values, vote attribution, open-set rejection<\/a><\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><a href=\"https:\/\/mastodon.social\/@Bgilbert1984\"><img loading=\"lazy\" decoding=\"async\" width=\"717\" height=\"690\" src=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/image-47.png\" alt=\"\" class=\"wp-image-4749\" srcset=\"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/image-47.png 717w, https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/wp-content\/uploads\/2025\/11\/image-47-300x289.png 300w\" sizes=\"auto, (max-width: 717px) 100vw, 717px\" \/><\/a><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/pdf\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/pdf\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] The Shapley Value of Classifiers in Ensemble Games &#8211; arXiv<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/pdf\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">We provide evidence that Shapley values are a useful decision metric for ensemble creation. Our results illustrate that model importance and &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/pdf\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/html\/2412.08916v1\" target=\"_blank\" rel=\"noreferrer noopener\">Measuring individual model importance based on contribution to &#8230;Forecast; Ensemble; Model importance; Shapley value; COVID-19 Forecasting &#8230; Thus, the obtained values may not precisely represent Shapley values &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/pdf\/2412.08916\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Measuring individual model importance based on contribution to &#8230;Keywords: Forecast; Ensemble; Model importance; Shapley value;. COVID &#8230; nascent stages of thinking about and understanding Shapley values.arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/html\/2404.00673v1\" target=\"_blank\" rel=\"noreferrer noopener\">A Survey of Privacy-Preserving Model Explanations &#8211; arXivWithout knowledge of the target&#8217;s data distribution, it becomes challenging to learn an attack model by observing Shapley values. To mitigate &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/html\/2505.23811v2\" target=\"_blank\" rel=\"noreferrer noopener\">LayerIF: Estimating Layer Quality for Large Language Models &#8230; &#8211; arXivMore recent work has used Shapley Values [56] to identify which layers contribute more to the model&#8217;s capabilities [57] . Other research has &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/html\/2508.12253v1\" target=\"_blank\" rel=\"noreferrer noopener\">Interpreting Time Series Forecasts with LIME and SHAP &#8211; arXivGradient boosting builds an ensemble of decision trees by &#8230; SHAP derives from Shapley values in cooperative game theory and &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/pdf\/2507.17306\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] A principled approach for comparing Variable Importance &#8211; arXivNote that Shapley values assigns nonzero importance to X1, even though it does not appear in the true model. Importance was estimated using. Gradient Boosting &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/html\/2404.12862v2\" target=\"_blank\" rel=\"noreferrer noopener\">A Guide to Feature Importance Methods for Scientific Inference &#8211; arXivTo account for the collaborative nature of FI, SAGE values use Shapley values &#8230; Ensemble Detectors and Background Knowledge. Progress in Artificial &#8230;arxiv.org<\/a><a href=\"http:\/\/www.arxiv.org\/pdf\/2006.07155v1\" target=\"_blank\" rel=\"noreferrer noopener\">Generalized SHAP: Generating multiple types of explanations in &#8230;Shapley&#8217;s values are model-agnostic, and the marginal contribution &#8230; Ensemble learning models trained on Sentinel-2 multispectral &#8230;arxiv.org<\/a><a href=\"https:\/\/arxiv.org\/pdf\/2508.00140\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Your Model Is Unfair, Are You Even Aware? Inverse &#8230; &#8211; arXivFor LightGBM, ELI5 traces ensemble decision tree paths to determine how each step impacts the prediction. &#8230; We neither varied the model&#8217;s level &#8230;arxiv.org<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">model class reliance fisher rudin10<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/1801.01489\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/1801.01489\" target=\"_blank\" rel=\"noreferrer noopener\">All Models are Wrong, but Many are Useful: Learning a Variable&#8217;s &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/1801.01489\" target=\"_blank\" rel=\"noreferrer noopener\">In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class.<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/1801.01489\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><a href=\"https:\/\/jmlr.csail.mit.edu\/papers\/volume20\/18-760\/18-760.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Learning a Variable&#8217;s Importance by Studying an Entire Class of &#8230;Applying this approach to study variable importance, we define model class reliance. (MCR) as the highest and lowest degree to which any well-performing model &#8230;jmlr.csail.mit.edu<\/a><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC8323609\/\" target=\"_blank\" rel=\"noreferrer noopener\">All Models are Wrong, but Many are Useful: Learning a Variable&#8217;s &#8230;In this paper, we propose model class reliance (MCR) as the range of VI values across all well-performing model in a prespecified class.pmc.ncbi.nlm.nih.gov<\/a><a href=\"https:\/\/www.researchgate.net\/publication\/322306535_Model_Class_Reliance_Variable_Importance_Measures_for_any_Machine_Learning_Model_Class_from_the_Rashomon_Perspective\" target=\"_blank\" rel=\"noreferrer noopener\">Model Class Reliance: Variable Importance Measures for any &#8230;Aaron Fisher \u00b7 Cynthia Rudin \u00b7 Francesca Dominici. Variable importance (VI) tools describe how much covariates contribute to a prediction model&#8217;s accuracy.researchgate.net<\/a><a href=\"http:\/\/www.rmwinslow.com\/bib\/paper\/fisher2018model.html\" target=\"_blank\" rel=\"noreferrer noopener\">Model class reliance: Variable importance measures for any &#8230;Papers; Model class reliance: Variable importance measures for any machine learning model class, from the \u201cRashomon\u201d perspective &#8230;rmwinslow.com<\/a><a href=\"https:\/\/www.nlab.org.uk\/project\/model-class-reliance-for-variable-importance-mcr\/\" target=\"_blank\" rel=\"noreferrer noopener\">Model Class Reliance for Variable Importance (MCR) &#8211; N\/LAB[1] Fisher, Rudin, and Dominici. \u201cAll Models are Wrong, but Many are Useful: Learning a Variable&#8217;s Importance by Studying an Entire Class of Prediction &#8230;nlab.org.uk<\/a><a href=\"https:\/\/users.cs.duke.edu\/~cynthia\/CourseNotes\/VariableImportance.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Variable Importance Duke Course NotesThis is called Model Class Reliance. Model Class Reliance. Let us define the set of all good models in class F to be the set of models with loss &#8230;users.cs.duke.edu<\/a><a href=\"https:\/\/nhsx.github.io\/AnalyticsUnit\/MCR.html\" target=\"_blank\" rel=\"noreferrer noopener\">Model Class Reliance for Demonstrating Variable ImportanceModel Class Reliance. MCR was developed by Fisher et al. to compute the feature importance bounds across all optimal models called the Rashomon set for Kernel ( &#8230;nhsx.github.io<\/a><a href=\"https:\/\/www.semanticscholar.org\/paper\/All-Models-are-Wrong%25252C-but-Many-are-Useful%25253A-Learning-Fisher-Rudin\/8ab8836f343f7234c56dac732b48c7760f4cf168\" target=\"_blank\" rel=\"noreferrer noopener\">All Models are Wrong, but Many are Useful: Learning a Variable&#8217;s &#8230;Model class reliance (MCR) is proposed as the range of VI values across all well-performing model in a prespecified class, which gives a more comprehensive &#8230;semanticscholar.org<\/a><a href=\"https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/fd512441a1a791770a6fa573d688bff5-Paper.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Model Class Reliance for Random Forests[7] Aaron Fisher, Cynthia Rudin, and Francesca Dominici. All models are wrong, but many are useful: Learning a variable&#8217;s importance by studying an entire &#8230;proceedings.neurips.cc<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">The Shapley Value of Classifiers in Ensemble Games10<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">The Shapley Value of Classifiers in Ensemble Games &#8211; arXiv<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">We argue that the Shapley value of models in these games is an effective decision metric for choosing a high performing subset of models from &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/arxiv.org\/abs\/2101.02153\" target=\"_blank\" rel=\"noreferrer noopener\">arxiv.org<\/a><a href=\"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482302\" target=\"_blank\" rel=\"noreferrer noopener\">The Shapley Value of Classifiers in Ensemble GamesWe argue that the Shapley value of models in these games is an effective decision metric for choosing a high-performing subset of models from the ensemble. Our &#8230;dl.acm.org<\/a><a href=\"https:\/\/www.research.ed.ac.uk\/files\/243305402\/2101.02153v2.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] The Shapley Value of Classifiers in Ensemble GamesThe Shapley value [41] of a classifier is the average marginal con- tribution of the model over the possible different permutations in which the ensemble can &#8230;research.ed.ac.uk<\/a><a href=\"https:\/\/github.com\/benedekrozemberczki\/shapley\" target=\"_blank\" rel=\"noreferrer noopener\">The official implementation of &#8220;The Shapley Value of Classifiers in &#8230;Shapley is a Python library for evaluating binary classifiers in a machine learning ensemble. The library consists of various methods to compute (approximate) &#8230;github.com<\/a><a href=\"https:\/\/www.semanticscholar.org\/paper\/18046c8ecb6a626055207327b9099e241e12f5d9\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] The Shapley Value of Classifiers in Ensemble GamesTroupe is defined &#8211; an efficient algorithm that allocates payoffs based on approximate Shapley values of the classifiers in ensemble games, arguing that the &#8230;semanticscholar.org<\/a><a href=\"https:\/\/www.researchgate.net\/publication\/348294376_The_Shapley_Value_of_Classifiers_in_Ensemble_Games\" target=\"_blank\" rel=\"noreferrer noopener\">(PDF) The Shapley Value of Classifiers in Ensemble GamesThe Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often &#8230;researchgate.net<\/a><a href=\"https:\/\/www.ijcai.org\/proceedings\/2022\/0778.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] The Shapley Value in Machine Learning &#8211; IJCAIFigure 1: The Shapley value can be used to solve cooperative games. An ensemble game is a machine learning application for it \u2013 models in an ensemble are &#8230;ijcai.org<\/a><a href=\"https:\/\/dblp.org\/rec\/conf\/cikm\/RozemberczkiS21\" target=\"_blank\" rel=\"noreferrer noopener\">The Shapley Value of Classifiers in Ensemble Games. &#8211; DBLPBibliographic details on The Shapley Value of Classifiers in Ensemble Games.dblp.org<\/a><a href=\"https:\/\/www.mdpi.com\/2076-3417\/13\/12\/7010\" target=\"_blank\" rel=\"noreferrer noopener\">Shapley Values as a Strategy for Ensemble Weights Estimation &#8211; MDPIThis study introduces a novel performance-based weighting scheme for ensemble learning using the Shapley value.mdpi.com<\/a><a href=\"https:\/\/ojs.aaai.org\/index.php\/AAAI\/article\/view\/29225\/30311\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Approximating the Shapley Value without Marginal ContributionsThe Shapley value, which is arguably the most popular ap- proach for assigning a meaningful contribution value to play- ers in a cooperative game, has recently &#8230;ojs.aaai.org<\/a><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">ensemble interpretability neural network RF signal classification10<\/h3>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41598-025-05027-8\" target=\"_blank\" rel=\"noreferrer noopener\"><\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41598-025-05027-8\" target=\"_blank\" rel=\"noreferrer noopener\">Ensemble learning for biomedical signal classification &#8211; Nature<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41598-025-05027-8\" target=\"_blank\" rel=\"noreferrer noopener\">The ensemble learning model presented in this framework included three classifiers: Random Forest (RF), Support Vector Machines (SVM), and &#8230;<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><a href=\"https:\/\/www.nature.com\/articles\/s41598-025-05027-8\" target=\"_blank\" rel=\"noreferrer noopener\">nature.com<\/a><a href=\"https:\/\/www.mdpi.com\/2504-446X\/8\/8\/391\" target=\"_blank\" rel=\"noreferrer noopener\">Convolutional Neural Network and Ensemble Learning-Based &#8230;This paper develops an ensemble learning ADS-B radio signal recognition framework. Firstly, the research analyzes the data content characteristics of the ADS-B &#8230;mdpi.com<\/a><a href=\"https:\/\/pmc.ncbi.nlm.nih.gov\/articles\/PMC11950602\/\" target=\"_blank\" rel=\"noreferrer noopener\">Ensemble of Deep Learning Architectures with Machine &#8230; &#8211; NIHThis study addresses the use of deep learning combined with machine learning classifiers (DLxMLCs) for pneumonia classification from chest X-ray (CXR) images.pmc.ncbi.nlm.nih.gov<\/a><a href=\"https:\/\/www.sciencedirect.com\/science\/article\/pii\/S1319157823000228\" target=\"_blank\" rel=\"noreferrer noopener\">A comprehensive review on ensemble deep learningThis review paper provides comprehensive reviews of the various strategies for ensemble learning, especially in the case of deep learning.sciencedirect.com<\/a><a href=\"https:\/\/www.researchgate.net\/publication\/393263702_Ensemble_learning_for_biomedical_signal_classification_a_high-accuracy_framework_using_spectrograms_from_percussion_and_palpation\" target=\"_blank\" rel=\"noreferrer noopener\">(PDF) Ensemble learning for biomedical signal classification: a high &#8230;By achieving a classification accuracy of 95.4%, the ensemble framework outperformed traditional classifiers in capturing subtle diagnostic variations. This &#8230;researchgate.net<\/a><a href=\"https:\/\/academic.oup.com\/bib\/article\/23\/1\/bbab377\/6371351\" target=\"_blank\" rel=\"noreferrer noopener\">Ensemble modeling with machine learning and deep learning to &#8230;We develop a protocol to combine the power of ML and DL to generate a set of simple rules that are easy to interpret with high prediction power.academic.oup.com<\/a><a href=\"https:\/\/lex-localis.org\/index.php\/LexLocalis\/article\/download\/800392\/1265\/20274\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] a machine learning ensemble approach for interference &#8230; &#8211; Lex localisIn order to improve CRN throughput and decrease interference, this research presents a new machine learning ensemble method that mixes Random. Forest (RF) with &#8230;lex-localis.org<\/a><a href=\"https:\/\/proceedings.scipy.org\/articles\/Majora-7ddc1dd1-003.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] Deep and Ensemble Learning to Win the Army RCO AI Signal &#8230;The methods discussed are capable of correctly classifying waveforms at -10 dB SNR with over 63% accuracy and signals at +10 dB SNR with over 95% accuracy from &#8230;proceedings.scipy.org<\/a><a href=\"https:\/\/inldigitallibrary.inl.gov\/sites\/sti\/sti\/Sort_67056.pdf\" target=\"_blank\" rel=\"noreferrer noopener\">[PDF] YOLO for Radio Frequency Signal ClassificationWe adapt YOLO for signal classification to enable the automatic and efficient identification of various signal types within a power spectral density image.inldigitallibrary.inl.gov<\/a><a href=\"https:\/\/arxiv.org\/html\/2510.09654v1\" target=\"_blank\" rel=\"noreferrer noopener\">TreeNet: Layered Decision Ensembles &#8211; arXivThe study investigates various types of models for the classification of medical image and proposed a hybrid model named Layered Decision &#8230;arxiv.org<\/a><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Explaining a series of models by propagating Shapley values &#8211; Nature<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">6: Explaining a series of models comprised of a convolutional neural network feature extractor and a gradient boosted tree classifier. figure 6.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">nature.com<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Neural network models and shapley additive explanations for a &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Shapley additive explanations provide interpretability for neural networks. \u2022. Interpretability analysis helps to modify neural network models. \u2022. An effective &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">sciencedirect.com<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ShapG: new feature importance method based on the Shapley value<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ShapG method is suitable for explaining any type of model, including complex neural network models or hybrid models. Report issue for preceding &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">arxiv.org<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Analysis of the Impact of Integrating Neural Networks into Ensemble &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">In our research, the ensemble learning approach that combines message passing neural network (MPNN) and conditional adversarial generative &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">mrs.org<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">(PDF) The Shapley Value of Classifiers in Ensemble Games<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&#8230; Shapley values were calculated from. the remaining 50% of graphs. E.1 Neural network ensembles. We created an ensemble of. m= 10. 3. neural &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">researchgate.net<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Ensemble Modeling for Neural Networks using large datasets<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Thus, the most cost-effective and effective method of using a large dataset to train a neural network is by ensemble modeling. Why use Ensemble &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">analyticsvidhya.com<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What are Shapley Values? | C3 AI Glossary Definitions &amp; Examples<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Convolutional Neural Network (CNN) \u00b7 Correlation \u00b7 Data Cleansing \u00b7 Data &#8230; Feature importance can be calculated by computing Shapley values for all the &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">c3.ai<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Explainability In Machine Learning: Top Techniques &#8211; Arize AI<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">DeepExplainer runs on deep learning frameworks to add explainability to neural network models by using DeepLIFT and Shapley values.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">arize.com<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An artificial neural network\u2212pharmacokinetic model and its &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The trigger of the current AI boom is deep learning, a machine-learning method using a deep neural network (DNN) in which the machine-learning &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">ascpt.onlinelibrary.wiley.com<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">How to explain neural networks using SHAP | Your Data Teacher<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">It is the SHAP approach. Let&#8217;s see how to use it for explain and interpret a neural network in Python. If you want to know more about neural &#8230;<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">yourdatateacher.com<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We convert per-model votes into auditable tracesand Shapley-like attributions for RF ensemble decisions. Weexpose hooks in classify_signal() to log per-model logits, calibrated probabilities, weights, and OSR gates, enablingtimeline and contribution analyses with negligible overhead.Our approach provides interpretable explanations for ensembleclassifications through vote tracing, model attribution, and disagreement analysis, enhancing trust and debugging capabilitiesfor RF signal&hellip;&nbsp;<\/p>\n","protected":false},"author":2,"featured_media":4749,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"","footnotes":""},"categories":[10,11],"tags":[],"class_list":["post-4748","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-signal-science","category-signal_scythe"],"_links":{"self":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/4748","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=4748"}],"version-history":[{"count":0,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/posts\/4748\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=\/wp\/v2\/media\/4749"}],"wp:attachment":[{"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=4748"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=4748"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/neurosphere-2.tail52f848.ts.net\/wordpress\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=4748"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}