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

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

## Submission File
For each `student_id` in the test set, you must predict a value for each of the two analytic measures (described on the [Data](https://www.kaggle.com/competitions/commonlit-evaluate-student-summaries/data) page). The file should contain a header and have the following format:

```
student_id,content,wording
000000ffffff,0.0,0.0
111111eeeeee,0.0,0.0
222222cccccc,0.0,0.0
333333dddddd,0.0,0.0
...
```