## Evaluation

In the "Concrete Strength - Regression" Kaggle competition, the performance of participants' models will be evaluated using the **Mean Squared Error (MSE)** metric. Let's break down what this means:

### Mean Squared Error (MSE)
- The MSE is a common metric used in regression tasks to assess the quality of predictions.
- It measures the average squared difference between the predicted values and the actual target values.
- Mathematically, the MSE is calculated as follows:

    $$\[ \text{MSE} = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2 \]$$

  where:
  - $$\(n\)$$ is the number of data points.
  - $$\(y_i\)$$ represents the actual concrete compressive strength (ground truth) for the $$\(i\)$$-th sample.
  - $$\(\hat{y}_i\)$$ represents the predicted concrete compressive strength for the $$\(i\)$$-th sample.

- Lower MSE values indicate better model performance, as they reflect smaller prediction errors.

### Submission File Format
Participants are required to submit their predictions in a CSV (Comma-Separated Values) file with the following format:

- **File Name:** `sample_submission.csv`
- **Columns:**
  - `Row ID`: A unique identifier for each data point.
  - `csMPa`: Predicted concrete compressive strength.

Example submission file:

```
Row ID,csMPa
1,45.8
2,62.3
3,28.6
...
```

Participants should ensure that their submission adheres to this format.