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Instructions for the data and code repository Results, post-processing workflow, and datasets for the research paper titled "Random Forest Classification and Solar Flares Data: Analysis and Validation". The folder contains three .csv files: the complete dataset (dataset.csv), the balanced training dataset (train_dataset.csv), and the testing dataset (test_dataset.csv). The folder also contains the result files from the research (.csv output files with predictions and .html files with evaluation metrics, etc.) exported from the JASP software. The number in each file name corresponds to the number of trees utilized in Random Forest modelling. In addition, the Python script for the post-processing workflow is provided, with comments located in the script. The soft range X-ray irradiance and VLF amplitude data were obtained from: National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed on: 24th June 2023. Worldwide archive of low-frequency data and observations (WALDO) Available online: https://waldo.world/. Accessed on: 24th June 2023.
{"references": ["National Centers for Environmental Information (NCEI) Available online: https://www.ncei.noaa.gov/. Accessed: 24th June 2023", "Worldwide Archive of Low-Frequency Data and Observations (WALDO) Available online: https://waldo.world/. Accessed: 24th June 2023"]}
Machine Learning, Solar flares, VLF ionospheric data, Anomaly classification, Anomaly detection, Class imbalance.
Machine Learning, Solar flares, VLF ionospheric data, Anomaly classification, Anomaly detection, Class imbalance.
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