Submissions are evaluated on [macro F1 score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html).

F1 is calculated as follows:

$$
F_1 = 2 * \frac{precision * recall}{precision + recall}
$$

where:

$$
precision = \frac{TP}{TP + FP}
$$

$$
recall = \frac{TP}{TP + FN}
$$


In "macro" F1 a separate F1 score is calculated for each class / label and then averaged.


## Submission File
For each `id` in the test set, you must predict a type of flower (or `label`). The file should contain a header and have the following format:

```
id,label
a762df180,0
24c5cf439,0
7581e896d,0
eb4b03b29,0
etc.
```