The evaluation metric for this competition is the samples-averaged \\(F\_1\\)-score (called F-Score Beta (Micro) on Kaggle) computed on the test set made of species presence-absence (PA) samples. In terms of machine learning, it is a multi-label classification task. The \\(F\_1\\)-score is an average measure of overlap between the predicted and actual set of species present at a given location and time.  
Each test PA sample \\( i \\) is associated with a set of ground-truth labels \\( Y\_i \\), namely the set of plant species (=speciesId) associated with a given combination of the columns **patchID** and **dayOfYear** (see the Data tab for details on the species observation data structure).  
For each sample, the submission will provide a list of labels, i.e. the set of species predicted present \\( \\widehat{Y}\_{i,1}, \\widehat{Y}\_{i,2}, \\dots, {\\widehat{Y}}\_{i,R\_i} \\).  
The micro \\(F\_1\\)-score is then computed using

\\\[ F\_1 = \\frac{1}{N} \\sum\_{i=1}^N \\frac{\\text{TP}\_i}{\\text{TP}\_i+(\\text{FP}\_i+\\text{FN}\_i)/2} \\\\ \\quad \\text{Where} \\begin{cases} \\text{TP}\_i =\\text{ Number of predicted species truly present, i.e. }|\\widehat{Y}\_i \\cap Y\_i |\\\\ \\text{FP}\_i =\\text{ Number of species predicted but absent, i.e. } |\\widehat{Y}\_i \\setminus Y\_i | \\\\ \\text{FN}\_i =\\text{ Number of species not predicted but present, i.e. } | Y\_i \\setminus \\widehat{Y}\_i |\\\\ \\end{cases} \\\]

Submission Format
=================

For each id in the test set, you must predict a set of species that occur at the given location. The file should contain a header and have the following format:
```
surveyId,predictions
1,1 52 10231
2,78 201 1243 1333 2310 4841
...
```
The submission format is a CSV file containing two columns for each sample (row):
*   `surveyId` column containing integers corresponding to the test sample ids, corresponding to unique combinations of patchID and dayOfYear column values.
*   `predictions` column containing space-delimited lists of the predicted species identifiers (column spId in training/validation datasets)

**For each sample (row), the predicted species identifiers must be ordered by increasing the value from left to right. No test sample is empty, and the test set only contains species from the train or validation set.**