What are the limitations of using naive Bayes algorithm to detect spam?

Disadvantages – A subtle issue with Naive-Bayes Classifier is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimation will be zero. A big data set is required for making reliable predictions of the probability of each class.

How accurate are spam filters?

‘Over 99\% accurate! ‘ ‘Zero critical false positives! ‘ ’10 times more effective than a human! ‘ Claims about the accuracy of spam filters abound in marketing literature and on company websites….

Why is naive Bayes good for spam filtering?

Naive Bayes spam filtering is a baseline technique for dealing with spam that can tailor itself to the email needs of individual users and give low false positive spam detection rates that are generally acceptable to users. It is one of the oldest ways of doing spam filtering, with roots in the 1990s.

Why is naive Bayes good for spam detection?

Spam Filtering With Bayes’ Rule, we want to find the probability an email is spam, given it contains certain words. We do this by finding the probability that each word in the email is spam, and then multiply these probabilities together to get the overall email spam metric to be used in classification.

What do spam filters look for?

Spam filters use predefined rules, or algorithms, to go through email messages. They look for emails with features that display the traits of spam-like emails. The algorithm then calculates the probability of that the message could be spam and assigns each part of the message a value.

How does spam filtering work?

Spam filters use a lot of different criteria to assess incoming email. After looking at each factor, spam filters assign a spam score. This score determines if an email will pass through the filter. Passing scores vary depending on the server, so an email could pass through some filters but not others.

What is spammer detection?

the Twitter spam detection approaches is presented that classifies the techniques based on their. ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users.

Why is spam detected?

This is called Spam Detection, and it is a binary classification problem. The reason to do this is simple: by detecting unsolicited and unwanted emails, we can prevent spam messages from creeping into the user’s inbox, thereby improving user experience.

Why is naive Bayes used for spam filtering?

Is naive Bayes good for spam detection?

This theorem, as explained in one of our previous articles, is mainly used for classification techniques in data analytics. The Naive Bayes theorem calculator pays an important role in spam detection of emails.