## Binary Classification (Depression vs. Healthy):
For the binary classification task to distinguish between individuals with depression and those who are healthy, we will use the F1 score as the evaluation metric. The F1 score is calculated as:

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

​Where:
Precision: The proportion of true positive predictions among all positive predictions.
$$\text{Precision} = \frac{TP}{TP + FP}$$

Recall: The proportion of true positive predictions among all actual positives.
$$\text{Recall} = \frac{TP}{TP + FN}$$

## Multi-Class Classification (Depression Severity):
For the multi-class classification task predicting the severity of depression, we will utilize the micro-average F1 score as the evaluation metric. The micro-average F1 score calculates the average F1 score across all classes, considering the overall true positive, false positive, and false negative counts. Each class's F1 score is computed using the same formula as in binary classification.

$$
\text{Micro-Average F1 Score} = \frac{2 \times {TP}}{2 \times {TP} + {FP} + {FN}}
$$

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## Submission File
Please refer to sample_submission.csv to format your results.