This competition is framed as a binary classification challenge, and its evaluation is based on the **F1 Score**, which you can find information on [GeeksforGeeks](https://www.geeksforgeeks.org/f1-score-in-machine-learning/) or [Wikipedia](https://en.wikipedia.org/wiki/F1_score) for a more academic explanation.

The F1 Score is defined as the harmonic mean of **Precision** and **Recall**, and is calculated using the following equation:

$$
\text{F1 Score} = 2 \cdot \frac{\text{Precision} \cdot \text{Recall}}{\text{Precision} + \text{Recall}}
$$

Where:
- **Precision** is the proportion of true positive predictions out of all positive predictions:
  $$
  \text{Precision} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Positives}}
  $$
- **Recall** is the proportion of true positive predictions out of all actual positives:
  $$
  \text{Recall} = \frac{\text{True Positives}}{\text{True Positives} + \text{False Negatives}}
  $$

The F1 Score provides a balance between Precision and Recall, making it a useful metric for imbalanced datasets where one class may dominate over the other.

## Submission File
For each ID in the test set, you must predict a target value. In this file, the TARGET column contains numeric values according to the following mapping:  
- **"stable"** → 0  
- **"chatter"** → 1  

The file should contain a header, saved in a `.csv` format, and have the following structure:

    ID,TARGET
    2,0
    5,0
    6,1
    etc.