Confusion matrix to check results I am a new user in StackExchange and a new learner of Data Science. I am working on better understanding how to estimate the results collected, specifically fake users extracted from a dataset running some analysis.
Using a specific algorithm, I found some users
User_Alg

user1
user2
user3
user28
user76
user67

and I would like to estimate accuracy of my algorithm comparing with the dataset which contains all the fake users manually labelled:
User_Dat
user1
user5
user28
user76
user67
user2
user29

As you can see, there are some users, in my extracted list (User_Alg), who are missing, i.e. not included in the list manually labelled (all the fake users in the dataset; User_Dat).
I have thought to use a confusion matrix to check the accuracy, but I would like to know from people with more experience in statistics and machine learning than me, if such method can be ok and how it looks like, or if you recommend another approach.
Thanks for your attention and your time.
A confusion matrix is indeed a very useful way to analyze the results of your experiment. It provides the exact number (or percentage) of instances with true class X predicted as class Y for all the possible classes. As such it gives a detailed picture of what the system classifies correctly or not.
But a confusion matrix is a bit too detailed if one wants to summarize the performance of the classifier as just one single value. This is useful especially when one wants to compare two different classifiers, since there's no general way to compare two confusion matrices. That's why people often use evaluation measures: for binary classification, the most common ones are:

Accuracy, which is simply the number of correct predictions divided by the total number of instances.
F-score, which itself is the harmonic mean of precision and recall. Quite ironically, F-score gives a more accurate picture of performance than accuracy because it takes into account the different types of possible errors.