I have a data set of video watching records in a 3G network. In this data set, 2 different kind of features are included:
- user-side information, e.g., age, gender, data plan and etc;
- Video watching records of these users, each of which associated with a download ratio and some detailed network condition metrics, say, download speed, RTT, and something similar.
Under the scenario of internet streaming, a video is divided into several chunks and downloaded to end device one by one, so we have download ratio = download bytes / file size in bytes
Now, Given this data set, I want to predict the download ratio of each video.
Since it is a regression problem, so I use gradient boosting regression tree as model and run 10-fold cross validation.
However, I have tried different model parameter configurations and even different models (linear regression, decision regress tree), the best root-mean-square error I can get is 0.3790, which is quite high, because if I don't use any complex models and just use the mean value of known labels as prediction values, then I can still have an RMSE of 0.3890. There is not obvious difference.
For this problem, I have some questions:
Does this high error rate imply that the label in data set is unpredictable?
Apart from the feature problem, is there any other possibilities? If yes, how can I validate them?