UnpredicTable数据集由从互联网表格中提取的少样本任务组成，旨在通过微调语言模型来提高其在少样本学习中的表现。数据集包含多个版本和子集，涵盖了广泛的任务类型，如多项选择、问答、文本分类等。数据集的创建基于WDC Web Table Corpus，并通过自动化的方式将表格转换为少样本学习任务。
Additional file 5: Table S4. Confusion matrix for Random Forest models attempting to predict host genus membership based on metagenome functions. Out-of-bag error rate was 58.35%.
This data accompanies the manuscript "Cross-platform normalization enables machine learning model training on microarray and RNA-seq data simultaneously" by Foltz, Taroni, and Greene. Please refer to
Biomass exports in thousand tons, and tons per capita for European countries. Our dataset has a 10.4% larger congruent dataset (to be used in various supervised or unsupervised learning models, such a
