An introduction to an informative and factual blog post titled “Confusion Matrix and cybersecurity”.
The complete lifecycle of Predictive modeling includes Data Cleaning, Pre-processing, and Data Wrangling. Now comes the stage where we try to fit in the model and the end goal is to achieve Low Bias and Low Variance. The most important step is Model Evaluation post Model Creation. The validation metrics are the parameters we look into to evaluate the performance of our model. We often fail to understand the resultant validation metrics. In this blog post, we will look into a commonly used metric called Confusion Matrix.
A confusion matrix is a table having two rows denominators and three columns, True Positives (TP), True Negatives(TN), False Positives (FP), and False Negatives (FN). It is very important to understand the output table of an evaluation in order to avoid potential problems that may arise due to misclassification or failure in predicting prospect values correctly without making mistakes.
The most commonly used confusion matrix is based on the squared difference between the probability of prediction and the actual outcome. It is widely used in model evaluation to differentiate between false positives and false negatives. A high value for the diagonal cells means the algorithm got most of its predictions correct. Here, the diagonal cells are assumed to be TP and TN, and the off-diagonal cells FP and FN. Now, coming back to our point of discussion, let’s look at a few examples which may explain this topic without being confusing
Example 1: Confusion Matrix Example [using actual values]
Let’s take an example where we have a discriminant analysis algorithm with two groups and try to evaluate our model on it with actual data as shown in the below graphic. final probability of prediction. The result will vary as per the level of accuracy we are looking for. For example, a score of 0.2 in a scoring system means that out of 100 classifiers in our model, only 20% are classified as the correct classifier. So we need to look at the 2 rows and 3 columns table to understand how we can interpret it properly. The first row in this table represents True Positives (TP), the second row represents True Negatives (TN), the third row represents False Positives (FP) and the last row represents False Negatives (FN). Model Evaluation post Model Creation. We often fail to understand the resultant performance Evaluation metrics.”.
The article will help provide an explanation for this process. This piece will also discuss what confusion matrix analysis can be used for as well as how it can be implemented into cybersecurity, among other things!
It’s hard to imagine how much easier it can possibly be.
