How is Precision calculated using the Confusion Matrix?

Precision is a crucial metric for evaluating the performance of a classification model, particularly in contexts where the positive class is of primary interest. Precision specifically measures the accuracy of positive predictions made by the model.

To understand how it is calculated using the Confusion Matrix, it's essential to recognize the components involved. In a Confusion Matrix, the key terms include:

• True Positives (TP): The number of correctly predicted positive instances.

• False Positives (FP): The number of incorrectly predicted positive instances.

• True Negatives (TN): The number of correctly predicted negative instances.

• False Negatives (FN): The number of incorrectly predicted negative instances.

Precision is defined mathematically as:

[ \text{Precision} = \frac{\text{True Positives (TP)}}{\text{True Positives (TP)} + \text{False Positives (FP)}} ]

This formula indicates that precision focuses specifically on the positive class predictions made by the model, assessing how many of those predictions were actually correct (True Positives) in relation to all instances that were predicted as positive (True Positives + False Positives).

This is particularly useful in scenarios where the cost of a false positive is high, as it directly informs how many