Comparison of the EODA algorithm with existing algorithms in terms of recall. by Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of precision. by Sunil Kumar (102321)

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Comparison of the EODA algorithm with existing algorithms in terms of F1-Score. by Sunil Kumar (102321)

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The relevant code in the manuscript can be found in the supporting information data file. by Xini Fang (20861990)

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Model-relevant variable data for external patients. by Lei Gao (6566)

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The proposed algorithm of the outlier detection method. by Sunil Kumar (102321)

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Results of RF algorithm screening factors. by Jintao Li (448681)

An example shows how XAI algorithm helps an expert to make a decision. by Shuvo Biswas (21728782)

<b>Long-term evaluation of pollution</b><b>dynamics</b><b>in West Lake Taihu, China: Enhanced WQI and TLI models for improved accuracy and management relevance</b> by Bo Chao (1916083)

Random Forest Model Results. by Md Shakhawath Hossain (17537148)

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Definitions of relevant SSA parameters. by Yeping Shi (14279118)

“…In tests comparing the performance of the SSA-BPNN, support vector machine (SVM), and random forest (RF) models, the SSA-BPNN achieves a 99.1% classification accuracy, better than the SVM and RF models. …”

Improved random forest algorithm. by Zhen Zhao (159931)

“…Compared with MDA-RF, the prediction accuracy of the improved RF built on the same subset increased by 1.7%, indicating that improving the bootstrap sampling of random forest by using the K-means++ clustering algorithm can enhance model accuracy to some extent. …”

The relevant data file for this article. by Linsha Liu (20378293)

Dataset for Fine-Tuning Code Generation Models: Kannada-English Algorithmic Statements and Python Code by Goutami Sooda (20714729)

Random Forest and Gradient Boosting algorithms. by Yvan J. Garcia-Lopez (20783591)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Predicting CO₂ Solubility in Diverse Ionic Liquids: A Data-Driven Approach Using Machine Learning Algorithms by Zahra Bastami (21474755)

Research data for paper: Efficient Event-based Delay Learning in Spiking Neural Networks by Balázs Mészáros (16890225)

“…<p dir="ltr">The data in this repository accompanies the paper 'Efficient Event-based Delay Learning in Spiking Neural Networks'</p><p dir="ltr">The data relates to 4 benchmarks:</p><ol><li>Spiking Heidelberg Digits (SHD).…”

Schematic diagram of random forest algorithm. by Imee V. Necesito (12749885)

Pseudocode for the missForestPredict algorithm. by Elena Albu (15181070)

“…Missing data in input variables often occur at model development and at prediction time. The missForestPredict R package proposes an adaptation of the missForest imputation algorithm that is fast, user-friendly and tailored for prediction settings. …”