简体   繁体   English

训练集的 R 平方比测试集的 R 平方大得多。 这对回归 model 意味着什么?

[英]R-squared for training set is way larger then the R-squared for testing set. What can that mean for the regression model?

Training Metrics:
R squared: 0.8099510921986353

Testing Metrics:
R squared: 0.17368322884835363

This is the result.这是结果。 I tried optimizing the data but the result does not change by much?我尝试优化数据但结果变化不大? What must be wrong with the model or data? model 或数据一定有什么问题? What can this difference in value mean for the model and data being used?对于 model 和正在使用的数据,这种价值差异意味着什么?

Probably your model is overfitted for your training dataset.可能您的 model 对于您的训练数据集过度拟合。 You could change the hyperparameters or try different resampling methods (eg try lower k for cross-validation).您可以更改超参数或尝试不同的重采样方法(例如,尝试使用较低的 k 进行交叉验证)。

声明:本站的技术帖子网页,遵循CC BY-SA 4.0协议,如果您需要转载,请注明本站网址或者原文地址。任何问题请咨询:yoyou2525@163.com.

 
粤ICP备18138465号  © 2020-2024 STACKOOM.COM