Implementation of random forest algorithm with parallel computing in R

Azizah, Nur; Riza, Lala Septem; Wihardi, Yaya

doi:10.1088/1742-6596/1280/2/022028

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Then we employed the random-forest algorithm using the 'randomForest' package in R 50 to compare the relationship between predictors and the gene-driven soil N cycling processes. The random-forest model ts many classi cation trees to a dataset, and then amalgamates the predictions from all the trees 51 .…”

Section: Global Mapping Prediction Of N Cycling Processes In Environm...mentioning

confidence: 99%

Natural ecosystems outperform agricultural lands in global soil nitrogen retention

Gu,

Yang,

et al. 2023

Preprint

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Nitrogen (N) is a vital nutrient essential for both soil microbiota and plant growth. The intersection of climate change and human activities has increased N inputs, leading to notable N losses that have concerning effects on human health. It is widely recognized that soils can retain large amounts of N. Therefore, understanding the global patterns of N cycling, influenced by regional environmental variations, is crucial for estimating soils' N retention abilities. In this paper, we analyzed 2,039 soil metagenomic samples to map out N cycling processes worldwide and revealed significant differences in N cycling among various ecosystems. Remarkably, natural ecosystems, especially grasslands, showed a much higher potential to retain soil N than agricultural lands. Moreover, natural ecosystems provided more consistent predictions of potential N retention relative to total soil N than agricultural systems. This divergence might be due to disruptions caused by intensive farming, which can lead to ecological imbalance in cultivated areas. These insights highlight the urgent need for global sustainable farming practices, aligning agricultural goals with ecosystem health.

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Section: Global Mapping Prediction Of N Cycling Processes In Environm...mentioning

confidence: 99%

Natural ecosystems outperform agricultural lands in global soil nitrogen retention

Gu,

Yang,

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Furthermore, the ensemble learning technique is based on a decision tree and widely used in various areas with almost ideal prediction [48]. Y. Mishina, R. Murata, Y. Yamauchi, T. Yamashita, and H. Fujiyoshi claim that RF is more robust than other famous models and have been utilized in many fields such as, computer visions and pattern recognition [49]. The common weakness in using RF is the processing needs more time when applied to large amounts of data because it has to build many tree models [50].…”

Section: ) Overview Of Machine Learning Techniquesmentioning

confidence: 99%

“…The common weakness in using RF is the processing needs more time when applied to large amounts of data because it has to build many tree models [50]. A large number of trees also require significant memory capacity [49]. The summarization of the main process to construct the RF is referred to [51].…”

Section: ) Overview Of Machine Learning Techniquesmentioning

confidence: 99%

Exploratory Study of Some Machine Learning Techniques to Classify the Patient Treatment

Sadikin¹,

Nurhaida²,

Puspita³

2021

IJACSA

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Numerous studies have been carried out on computation and its applications to medical data with proven benefits for improving the quality of public health. However, not all research results or practical applications can be applied to all conditions but must be in accordance with the various contexts such as community culture, geographical, or citizen behaviors. Unfortunately, the use of digital data in Indonesia is still very limited. The study objective is to assess various data mining techniques to utilize data from laboratory test results collected from a private hospital in Indonesia in predicting the next patient treatment. Furthermore, various machine learning classification techniques were explored for the purpose. Based on the experiments, it was concluded that XGBoost with hyperparameter tuning produced the best accuracy level at 0.7579, compared to other classifiers. A better level of accuracy can be obtained by enriching the type of dataset used, such as the patient's medical record history.

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