Mastering Machine Learning for Penetration Testing

Performance evaluation

Evaluation is a key step in every methodological operation. After building a product or a system, especially a machine learning model, we need to have a clear vision about its performance, to make sure that it will act as intended later on. In order to evaluate a machine learning performance, we need to use well-defined parameters and insights. To compute the different evaluation metrics, we need to use four important parameters:

• True positive
• False positive
• True negative
• False negative

The notations for the preceding parameters are as follows:

• tp: True positive
• fp: False positive
• tn: True negative
• fn: False negative

There are many machine learning evaluation metrics, such as the following:

• Precision: Precision, ...