地球资源数据云——数据资源详情
线性回归
该数据集《Linear Regression》主要用于回归/预测任务，数据形态以文本为主，应用场景偏向天文科学。 题目说明：Randomly created dataset for linear regression 任务类型：文本回归/预测。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：test.csv, train.csv。 Context This is probably the dumbest dataset on Kaggle. The whole point is, however, to provide a common dataset for linear regression. Although such a dataset can easily be generated in Excel with random numbers, results would not be comparable. Content The training dataset is a CSV file with 700 data pairs (x,y). The x - values are numbers between 0 and 100. The corresponding y - values have been generated using the Excel function NORMINV(RAND(), x, 3). Consequently, the best estimate for y should be x. The test dataset is a CSV file with 300 data pairs. Acknowledgements
摘要概览
该数据集《Linear Regression》主要用于回归/预测任务，数据形态以文本为主，应用场景偏向天文科学。 题目说明：Randomly created dataset for linear regression
任务类型：文本回归/预测。
建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。
评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。
可用文件：test.csv, train.csv。
Context
This is probably the dumbest dataset on Kaggle. The whole point is, however, to provide a common dataset for linear regression. Although such a dataset can easily be generated in Excel with random numbers, results would not be comparable.
Content
The training dataset is a CSV file with 700 data pairs (x,y). The x - values are numbers between 0 and 100. The corresponding y - values have been generated using the Excel function NORMINV(RAND(), x, 3). Consequently, the best estimate for y should be x. The test dataset is a CSV file with 300 data pairs.
Acknowledgements