The evaluation pages describes how submissions will be scored and how students should format their submissions. You don't need a sub-title at the top; the page title appears above. For an example of a high quality evaluation page, see the 2013 KDD Cup page, which we have included below:

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The evaluation metric for this competition is [Root mean square error]. The root mean square error, commonly used to measure the accuracy in regression problem:

\[ RMSE = \sqrt{ \cfrac{\sum\limits_{i=1}^n \ (y_i - \hat{y}_i)^2}{n} } \]

where \[n\] is the number of observations in the test data set; \[y_i\] is the \[i^{th}\] true value and \[\hat{y}_i\] is the \[i^{th}\] predicted value

Precision is the ratio of true positives \(\text{tp}\) to all predicted positives \(\text{tp} + \text{fp}\). Recall is the ratio of true positives \(\text{tp}\) to all actual positives \(\text{tp} + \text{fn}\). The F1 score is given by:

\[ \text{F1} = 2\frac{\text{p} \cdot \text{r}}{\text{p}+\text{r}}\ \ \mathrm{where}\ \ \text{p} = \frac{\text{tp}}{\text{tp}+\text{fp}},\ \ \text{r} = \frac{\text{tp}}{\text{tp}+\text{fn}} \]

The F1 metric weights recall and precision equally, and a good retrieval algorithm will maximize both precision and recall simultaneously. Thus, moderately good performance on both will be favored over extremely good performance on one and poor performance on the other.

## Submission Format

**For every author in the dataset**, submission files should contain two columns: `AuthorId` and `DuplicateAuthorIds`. `DuplicateAuthorIds` should be a space-delimited list. Every `AuthorId` counts as its own duplicate, and every duplicate should be listed under each of its respective ids. For example, if you suspect author A, B, and C are the same, you should list `A,A B C`, `B,B A C`, `C,C A B`.


The file should contain a header and have the following format:
```
AuthorId,DuplicateAuthorIds
1,1
```