**Confusion Matrix:**

Consider a binary classification problem where we aim to predict whether an email is spam or not spam. The confusion matrix for this scenario is structured as follows:

```
| Predicted Not Spam | Predicted Spam |
---------------------|---------------------|-----------------|
Actual Not Spam | TN | FP |
---------------------|---------------------|-----------------|
Actual Spam | FN | TP |
```

Here:

**TN (True Negative):** Emails correctly predicted as not spam.
**FP (False Positive):** Emails incorrectly predicted as spam (Type I error).
**FN (False Negative):** Emails incorrectly predicted as not spam (Type II error).
**TP (True Positive):** Emails correctly predicted as spam.

**Accuracy:**

Accuracy measures the overall correctness of the model, providing the ratio of correctly predicted instances to the total instances.

**Precision:**

Precision gauges the accuracy of positive predictions, answering the question: Of the instances predicted as positive, how many are truly positive?

**Recall (Sensitivity or True Positive Rate):**

Recall assesses the model’s ability to capture all positive instances, answering: Of all actual positive instances, how many were predicted correctly?

**F1 Score:**

The F1 score, being the harmonic mean of precision and recall, provides a balanced measure between the two metrics. These evaluation metrics collectively offer a comprehensive assessment of a model’s performance, crucial in scenarios with imbalanced classes.