Matrix-ID_Detailed results for all tested models for Signal prediction and Retention Time prediction

Contains the dataset of 2,604 chromatographic measurements generated from the analysis of 186 training molecules across 14 LC methods, as well as the detailed performance results of all machine-learning models evaluated. It reports the cross-validation outcomes for the five tested algorithms (RF, XGBoost, Extra Trees, Ridge, and ElasticNet) used for signal/no-signal classification and RT prediction.本数据集包含通过14种液相色谱（Liquid Chromatography, LC）方法对186个训练分子开展分析所生成的2604条色谱测量数据，同时涵盖所有经评估的机器学习模型的详细性能结果。本数据集报告了用于信号/非信号分类与保留时间（Retention Time, RT）预测的5种测试算法，即随机森林（Random Forest, RF）、XGBoost、极端随机树（Extra Trees）、岭回归（Ridge）与弹性网回归（ElasticNet）的交叉验证结果。