Interpretable machine learning approach in estimating traffic volume on low-volume roadwaysTraffic volumeLow-volume roadsAADTMachine learningInterpretable machine learningMany state and local agencies are currently facing challenges concerning the collection and estimation of traffic volumes, particularly regarding...
Strategies combining high-throughput (HT) and machine learning (ML) to accelerate the discovery of promising new materials have garnered immense attention in recent years. The knowledge of new guiding principles is usually scarce in such studies, essentially due to the ‘black-box’ nature of the...
By leveraging advanced machine learning algorithms and extensive datasets, this approach can significantly improve early-stage obesity prediction by uncovering its underlying causes. 展开 关键词: Obesity Logistic regression Machine learning algorithms Stacking Pipelines Feature extraction Diabetes ...
This work demonstrates that the XGBoost ML approach is interpretable and feasible in the extraction of decisive parameters for properties of Fe-based magnetic MGs, which might allow us to efficiently design high-performance glassy materials. npj Computational Materials (2020) 6:187 ; https://doi....
: An Interpretable Machine Learning Approach : An inter- pretable machine learning approach. In PloS one.L. Arras, F. Horn, G. Montavon, K.-R. Mu¨ller, and W. Samek, ""what is relevant in a text document?": An interpretable machine learning approach," Plos ONE,... L Arras,F ...
文献阅读笔记-《An interpretable approach for personality recognition from social media》 Abstract 1.可以从社交网络用户行为数据识别用户人格特质;分析用户文本可以识别用户人格。 2.现有研究:使用心理学词典/machine learning/deep learning预测人格,but效果不好or缺乏可解释性。
Highlights Utilizing an interpretable machine learning approach, we introduce a framework aimed at predicting the fundraising performance of environmental ... LiuZhanyu,HuSaiquan 被引量: 0发表: 2024年 The role of trust management in reward-based crowdfunding Purpose – The purpose of this paper is...
Of course, when we use a machine learning algorithm, we do not fix the parameters ahead of time, then sample both datasets. We sample the training set, then use it to choose the parameters to reduce training set error, then sample the test set. Under this process, the expected test erro...
Herein we propose a machine-learning (ML)-based approach for crystal system and space group classification based on powder X-ray diffraction (XRD) patterns as a proof of concept using simulated patterns. Our tree-ensemble-based ML model works with nearly or over 90% accuracy for crystal system...
Here, we propose an interpretable machine learning approach in which functional profiles of microbiota samples, with a direct interpretation, are first obtained from shotgun sequencing and subsequently used as features for predicting CRC in the patient donor of the sample. Moreover, in the prediction...