### Metric
The evaluation metric for this competition is Mean F1-Score. The F1 score, commonly used in information retrieval, measures accuracy using the statistics precision p and recall r. Precision is the ratio of true positives *tp* to all predicted positives *tp+fp*. Recall is the ratio of true positives *tp* to all actual positives *tp+fn*. The F1 score is given by:

\\( F1 = 2\frac{p \cdot r}{p+r}\ \ \mathrm{where}\ \ p = \frac{tp}{tp+fp},\ \ r = \frac{tp}{tp+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.

Mean F1-Score calculates F1 metrics for each label, and finds their average, weighted by support, i.e. the number of true instances for each label. Calculated with http://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html using average='weighted'.
In your report you are encouraged to show your results in various metrics such as precision,  accuracy, recall, etc.,  and explain the relations among them.  

### Submission File

For each id in the test set, you must predict the target variable "income". The file should be in csv format and contain a header. See the sample submission "zero_solution.csv" under the Data tab for a complete example.