Compare classification performance. Papa et al. (2012) applied sequencing data to supervised learning classification algorithms using a software pipeline called Synthetic Learning in Microbial Ecology (SLiME), which utilizes relevant metadata as classification labels. They achieved an average AUC of 0.83 on fecal samples over three repeated 10-fold cross-validation. We trained both the Modified DeepInsight and the Original DeepInsight models 1,000 trials using Optuna and selected the maximum AUC value as the final result.
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https://figshare.com/articles/dataset/Compare_classification_performance_Papa_et_al_2012_applied_sequencing_data_to_supervised_learning_classification_algorithms_using_a_software_pipeline_called_Synthetic_Learning_in_Microbial_Ecology_SLiME_which_utilizes_relevant_metadata_as_c/28810220
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Compare classification performance. Papa et al. (2012) applied sequencing data to supervised learning classification algorithms using a software pipeline called Synthetic Learning in Microbial Ecology (SLiME), which utilizes relevant metadata as classification labels. They achieved an average AUC of 0.83 on fecal samples over three repeated 10-fold cross-validation. We trained both the Modified DeepInsight and the Original DeepInsight models 1,000 trials using Optuna and selected the maximum AUC value as the final result.
创建时间：
2025-04-16
