Auswahl der wissenschaftlichen Literatur zum Thema „Partial Dependence Plot“
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Zeitschriftenartikel zum Thema "Partial Dependence Plot"
Yan, Miaomiao, und Yindong Shen. „Traffic Accident Severity Prediction Based on Random Forest“. Sustainability 14, Nr. 3 (02.02.2022): 1729. http://dx.doi.org/10.3390/su14031729.
Der volle Inhalt der QuelleDewan, Isha, und Subhash Kochar. „SOME NEW APPLICATIONS OF P–P PLOTS“. Probability in the Engineering and Informational Sciences 27, Nr. 3 (28.03.2013): 353–66. http://dx.doi.org/10.1017/s0269964813000077.
Der volle Inhalt der QuelleLee, Changro. „Training and Interpreting Machine Learning Models: Application in Property Tax Assessment“. Real Estate Management and Valuation 30, Nr. 1 (01.03.2022): 13–22. http://dx.doi.org/10.2478/remav-2022-0002.
Der volle Inhalt der QuelleFu, Xiao. „The D e (T, t) plot: A straightforward self-diagnose tool for post-IR IRSL dating procedures“. Geochronometria 41, Nr. 4 (01.12.2014): 315–26. http://dx.doi.org/10.2478/s13386-013-0167-9.
Der volle Inhalt der QuelleTran, Van Quan. „Predicting and Investigating the Permeability Coefficient of Soil with Aided Single Machine Learning Algorithm“. Complexity 2022 (25.09.2022): 1–18. http://dx.doi.org/10.1155/2022/8089428.
Der volle Inhalt der QuelleWu, Zihao, Yiyun Chen, Yuanli Zhu, Xiangyang Feng, Jianxiong Ou, Guie Li, Zhaomin Tong und Qingwu Yan. „Mapping Soil Organic Carbon in Floodplain Farmland: Implications of Effective Range of Environmental Variables“. Land 12, Nr. 6 (08.06.2023): 1198. http://dx.doi.org/10.3390/land12061198.
Der volle Inhalt der QuellePatterson, L. D., und G. Blouin-Demers. „Partial support for food availability and thermal quality as drivers of density and area used in Yarrow’s Spiny Lizards (Sceloporus jarrovii)“. Canadian Journal of Zoology 98, Nr. 2 (Februar 2020): 105–16. http://dx.doi.org/10.1139/cjz-2019-0166.
Der volle Inhalt der QuelleKhoerunnisa, Fitri, Aaron Morelos-Gomez, Hideki Tanaka, Toshihiko Fujimori, Daiki Minami, Radovan Kukobat, Takuya Hayashi et al. „Metal–semiconductor transition like behavior of naphthalene-doped single wall carbon nanotube bundles“. Faraday Discuss. 173 (2014): 145–56. http://dx.doi.org/10.1039/c4fd00119b.
Der volle Inhalt der QuelleChang, Shih-Chieh, Chan-Lin Chu, Chih-Kuang Chen, Hsiang-Ning Chang, Alice M. K. Wong, Yueh-Peng Chen und Yu-Cheng Pei. „The Comparison and Interpretation of Machine-Learning Models in Post-Stroke Functional Outcome Prediction“. Diagnostics 11, Nr. 10 (28.09.2021): 1784. http://dx.doi.org/10.3390/diagnostics11101784.
Der volle Inhalt der QuelleShiroyama, Risa, Manna Wang und Chihiro Yoshimura. „Effect of sample size on habitat suitability estimation using random forests: a case of bluegill, Lepomis macrochirus“. Annales de Limnologie - International Journal of Limnology 56 (2020): 13. http://dx.doi.org/10.1051/limn/2020010.
Der volle Inhalt der QuelleDissertationen zum Thema "Partial Dependence Plot"
Danesh, Alaghehband Tina Sadat. „Vers une conception robuste en ingénierie des procédés. Utilisation de modèles agnostiques de l'interprétabilité en apprentissage automatique“. Electronic Thesis or Diss., Toulouse, INPT, 2023. http://www.theses.fr/2023INPT0138.
Der volle Inhalt der QuelleRobust process design holds paramount importance in various industries, such as process and chemical engineering. The nature of robustness lies in ensuring that a process can consistently deliver desired outcomes for decision-makers and/or stakeholders, even when faced with intrinsic variability and uncertainty. A robustly designed process not only enhances product quality and reliability but also significantly reduces the risk of costly failures, downtime, and product recalls. It enhances efficiency and sustainability by minimizing process deviations and failures. There are different methods to approach the robustness of a complex system, such as the design of experiments, robust optimization, and response surface methodology. Among the robust design methods, sensitivity analysis could be applied as a supportive technique to gain insights into how changes in input parameters affect performance and robustness. Due to the rapid development and advancement of engineering science, the use of physical models for sensitivity analysis presents several challenges, such as unsatisfied assumptions and computation time. These problems lead us to consider applying machine learning (ML) models to complex processes. Although, the issue of interpretability in ML has gained increasing importance, there is a growing need to understand how these models arrive at their predictions or decisions and how different parameters are related. As their performance consistently surpasses that of other models, such as knowledge-based models, the provision of explanations, justifications, and insights into the workings of ML models not only enhances their trustworthiness and fairness but also empowers stakeholders to make informed decisions, identify biases, detect errors, and improve the overall performance and reliability of the process. Various methods are available to address interpretability, including model-specific and model-agnostic methods. In this thesis, our objective is to enhance the interpretability of various ML methods while maintaining a balance between accuracy and interpretability to ensure decision-makers or stakeholders that our model or process could be considered robust. Simultaneously, we aim to demonstrate that users can trust ML model predictions guaranteed by model-agnostic techniques, which work across various scenarios, including equation-based, hybrid, and data-driven models. To achieve this goal, we applied several model-agnostic methods, such as partial dependence plots, individual conditional expectations, accumulated local effects, etc., to diverse applications
Gomes, Rahul. „Incorporating Sliding Window-Based Aggregation for Evaluating Topographic Variables in Geographic Information Systems“. Diss., North Dakota State University, 2019. https://hdl.handle.net/10365/29913.
Der volle Inhalt der QuelleNational Science Foundation (Award OIA-1355466)
Buchteile zum Thema "Partial Dependence Plot"
Guseo, R. „Partial Mixed Effects Split-Plot Design under Unknown Spatial Dependence“. In AMST ’99, 859–66. Vienna: Springer Vienna, 1999. http://dx.doi.org/10.1007/978-3-7091-2508-3_98.
Der volle Inhalt der QuelleMolnar, Christoph, Timo Freiesleben, Gunnar König, Julia Herbinger, Tim Reisinger, Giuseppe Casalicchio, Marvin N. Wright und Bernd Bischl. „Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process“. In Communications in Computer and Information Science, 456–79. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44064-9_24.
Der volle Inhalt der QuelleBaniecki, Hubert, Wojciech Kretowicz und Przemyslaw Biecek. „Fooling Partial Dependence via Data Poisoning“. In Machine Learning and Knowledge Discovery in Databases, 121–36. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26409-2_8.
Der volle Inhalt der QuelleMuschalik, Maximilian, Fabian Fumagalli, Rohit Jagtani, Barbara Hammer und Eyke Hüllermeier. „iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios“. In Communications in Computer and Information Science, 177–94. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44064-9_11.
Der volle Inhalt der QuelleMolnar, Christoph, Gunnar König, Julia Herbinger, Timo Freiesleben, Susanne Dandl, Christian A. Scholbeck, Giuseppe Casalicchio, Moritz Grosse-Wentrup und Bernd Bischl. „General Pitfalls of Model-Agnostic Interpretation Methods for Machine Learning Models“. In xxAI - Beyond Explainable AI, 39–68. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_4.
Der volle Inhalt der QuelleBloom, Barthe. „Chapter 2. Life at the intersection“. In Constructional Approaches to Nordic Languages, 24–54. Amsterdam: John Benjamins Publishing Company, 2023. http://dx.doi.org/10.1075/cal.37.02blo.
Der volle Inhalt der QuelleRay, Robert B. „Yells“. In The ABCs of Classic Hollywood, 224–25. Oxford University PressNew York, NY, 2008. http://dx.doi.org/10.1093/oso/9780195322910.003.0077.
Der volle Inhalt der QuelleKlinger, William, und Denis Kuljiš. „Oberkrainer Communism“. In Tito's Secret Empire, 51–56. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197572429.003.0008.
Der volle Inhalt der Quelle„Neighbourhood Influences on Vehicle-Pedestrian Crash Severity“. In Big Data Analytics in Traffic and Transportation Engineering, 102–21. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7943-4.ch005.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Partial Dependence Plot"
Rivas, Pablo, Pamela Harper, John Cary und William Brown. „ML-Based Feature Importance Estimation for Predicting Unethical Behaviour under Pressure“. In LatinX in AI at International Conference on Machine Learning 2019. Journal of LatinX in AI Research, 2019. http://dx.doi.org/10.52591/lxai201906155.
Der volle Inhalt der QuelleTEIXEIRA POLEZ, RODRIGO, Luiz Henrique Antunes Rodrigues und FELIPE FERREIRA BOCCA. „Partial dependence plots for inspecting machine learning models of sugarcane yield“. In XXIV Congresso de Iniciação Científica da UNICAMP - 2016. Campinas - SP, Brazil: Galoa, 2016. http://dx.doi.org/10.19146/pibic-2016-50805.
Der volle Inhalt der QuelleMiyaji, Renato Okabayashi, Felipe Valencia Almeida und Pedro Luiz Pizzigatti Corrêa. „Evaluating the Explainability of Machine Learning Classifiers: A case study of Species Distribution Modeling in the Amazon“. In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/kdmile.2023.232929.
Der volle Inhalt der QuelleEswaran, M., und U. K. Saha. „Low Steeping Waves Simulation in a Vertical Excited Container Using σ Transformation“. In ASME 2009 28th International Conference on Ocean, Offshore and Arctic Engineering. ASMEDC, 2009. http://dx.doi.org/10.1115/omae2009-80248.
Der volle Inhalt der QuelleNyantekyi-Kwakye, B., S. Clark, M. F. Tachie, J. Malenchak und G. Muluye. „Flow Characteristics and Structure of 3D Turbulent Offset Jets“. In ASME 2014 4th Joint US-European Fluids Engineering Division Summer Meeting collocated with the ASME 2014 12th International Conference on Nanochannels, Microchannels, and Minichannels. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/fedsm2014-21276.
Der volle Inhalt der QuelleAnand, Sushant, und R. C. Arora. „Comparative Analysis of Different Thermal Conductivity Models for Nanofluids in a Square Enclosure Under Natural Convection Conditions“. In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82576.
Der volle Inhalt der QuelleArgente del Castillo Martínez, Juan Pablo, und Isabel P. Albaladejo. „Understanding the effects of Covid-19 on P2P hospitality: Comparative classification analysis for Airbnb-Barcelona.“ In CARMA 2022 - 4th International Conference on Advanced Research Methods and Analytics. valencia: Universitat Politècnica de València, 2022. http://dx.doi.org/10.4995/carma2022.2022.15091.
Der volle Inhalt der QuelleAdeeyo, Yisa Ademola, Anuola Ayodeji Osinaike und Gamaliel Olawale Adun. „Estimation of Fluid Saturation Using Machine Learning Algorithms: A Case Study of Niger Delta Sandstone Reservoirs“. In SPE Reservoir Characterisation and Simulation Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/212696-ms.
Der volle Inhalt der QuellePirrone, Marco, Satria Andrianata, Sara Moriggi, Giuseppe Galli und Simone Riva. „Full Analytical Modeling Of Intrawell Chemical Tracer Concentration For Robust Production Allocation In Challenging Environments“. In SPE Annual Technical Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/206245-ms.
Der volle Inhalt der QuelleButryn, Krzysztof, und Edward Preweda. „Analysis of the Impact of Quantitative and Qualitative Price-setting Attributes on a Market of Real Estate Intended for the Purpose of the Transformer Stations on the Example of Krakow“. In Environmental Engineering. VGTU Technika, 2017. http://dx.doi.org/10.3846/enviro.2017.177.
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