Submissions are scored using MCRMSE, mean columnwise root mean squared error:

$$
\textrm{MCRMSE} = \frac{1}{N\_{t}}\sum\_{j=1}^{N\_{t}}\sqrt{\frac{1}{n} \sum\_{i=1}^{n} (y\_{ij} - \hat{y}\_{ij})^2}
$$
where \\(N\_t\\) is the number of scored ground truth target columns, and \\(y\\) and \\(\hat{y}\\) are the actual and predicted values, respectively.

## Submission File
For each `text_id` in the test set, you must predict a value for each of the six analytic measures (described on the [Data](https://www.kaggle.com/competitions/feedback-prize-english-language-learning/data) page). The file should contain a header and have the following format:

```
text_id,cohesion,syntax,vocabulary,phraseology,grammar,conventions
0000C359D63E,3.0,3.0,3.0,3.0,3.0,3.0
000BAD50D026,3.0,3.0,3.0,3.0,3.0,3.0
00367BB2546B,3.0,3.0,3.0,3.0,3.0,3.0
003969F4EDB6,3.0,3.0,3.0,3.0,3.0,3.0
...
```