# Evaluation Metric 

The evaluation metric for this Kaggle competition is [Mean F1-Score](https://en.wikipedia.org/wiki/F-score). The F1 score, commonly used in information retrieval, measures accuracy using the precision of the statistics \( \text{p} \) and recall \( \text{r} \). Precision is the ratio of true positives \( \text{tp} \) to all predicted positives \( \text{tp+fp} \). The recall is the ratio of true positives \( \text{tp} \) to all actual positives \( \text{tp+fp} \). 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. In your report, you can use various metrics to demonstrate your prediction results and you need to briefly describe the metrics you choose. Matrices and diagrams are also welcomed with your analysis.