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C&EN1mon
Identification of Laminar Structure in the Yingxiongling Shale Oil Sediment in China with Random Forests and SHAP Analysis
In addition, from the TOC and (S1 + S2) distribution diagrams (Figure 3), it can also be seen ... As mentioned before, the random forest algorithm can provide relatively accurate prediction results, ...
Frontiers3mon
Random forest grid fault prediction based on genetic algorithm optimization
In the data pre-processing module, the acquired meteorological data are first subjected to data cleaning, followed by feature selection of the cleaned data using the random forest algorithm. During ...
IEEE1y
Analyzing the Effects of K-Means and Random Forest Algorithms on Resume Classification through Manipulation of Cluster Sizes and Datasets
Questionnaires in the MCQ format are used in Module 1 to collect input from user ... followed by TF-IDF vectorization and Random Forest Algorithm to classify them based on similarity to job title.
SciELO1y
Detection of Eyebolt Faults Using a Random Forest Ensemble Model Based on Multiple High-Frequency Electromagnetic Parameters
This paper presents an eyebolt structural fault detection system, based on the analysis of multiple electromagnetic parameters through a random forest classifier trained by both measurements and ...
Frontiers3y
Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis
Background and Aims: This study aimed to develop an interpretable random forest model for predicting severe acute ... All statistical analysis were performed in the R and STATA software. A data flow ...
The EMBO Journal4y
DOMINO: a network‐based active module identification algorithm with reduced rate of false calls
Algorithms for active module ... random permutation of the original gene activity scores of this dataset. The histograms show the distributions of GO enrichment scores obtained for the modules ...