A confusion matrix is a table that is often used to describe the performance of a classification model (or “classifier”) on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing.
Two types of errors
• True Positive (TP): The amount of attack detected when it is actually attack.
• True Negative (TN): The amount of normal detected when it is actually normal.
• False Positive (FP): The amount of attack detected when it is actually normal (False alarm).
• False Negative (FN): The amount of normal detected when it is actually attack.
Common forms of cybercrime
- phishing: using fake email messages to get personal information from internet users;
- misusing personal information (identity theft);
- hacking: shutting down or misusing websites or computer networks;
- spreading hate and inciting terrorism;
- distributing child pornography;
How confusion matrix helps in order to escape Cyber crime cases
A confusion matrix is a tabular summary of the number of correct and incorrect
predictions made by a classifier. It is used to measure the performance of a classification model. It
can be used to evaluate the performance of a classification model through the calculation of
performance metrics like accuracy, precision, recall, and F1-score.
Need for Confusion Matrix in Machine learning:
o It evaluates the performance of the classification models, when they make predictions on
test data, and tells how good our classification model is.
o It not only tells the error made by the classifiers but also the type of errors such as it is either
type-I or type-II error.
o With the help of the confusion matrix, we can calculate the different parameters for the
model, such as accuracy, precision, etc.
The confusion matrix is a matrix used to determine the performance of the classification
models for a given set of test data. It can only be determined if the true values for test data are
known. The matrix itself can be easily understood and implemented to test a ML model.