How does the naive Bayes algorithm help in classifying emails to the spam category explain with an example?
Naive bayes is commonly used to construct spam filters that classify emails as spam or ham (not spam). If on the other hand, an email contains the word money, then there’s an 80% chance that it’s spam. According to Bayes Theorem, the probability that an email is spam given that it contains “word”.
Which algorithm is used for spam email classification?
Some of the most popular spam email classification algorithms are Multilayer Perceptron Neural Networks (MLPNNs) and Radial Base Function Neural Networks (RBFNN). Researchers used MLPNN as a classifier for spam filtering but not many of them used RBFNN for classification.
Can we use algorithm for real time classification of emails?
Generally, email analysis can be classified under text categorization in its most activities. Algorithms such as: VSM, KNN, Ripper, Maximum Entropy (MaxEnt), Winnow, ANN are examples of algorithms used in email analysis. This can be further used for the real time prediction of spam emails.
How does naive Bayes classifier work for email filtering?
Absolutely right! In this age and time of data analytics & machine learning, automated filtering of emails happens via algorithms like Naive Bayes Classifier, which apply the basic Bayes Theorem on the data.
Which is the most powerful Bayes classifier algorithm?
One of the most simple yet powerful classifier algorithms, Naive Bayes is based on Bayes’ Theorem Formula with an assumption of independence among predictors.
Is the naive Bayes assumption true for email?
The Naive Bayes assumption implies that the words in an email are conditionally independent, given that you know that an email is spam or not. Clearly this is not true. Neither the words of spam or not-spam emails are drawn independently at random. However, the resulting classifiers can work well in practice even if this assumption is violated.
Is the answer yes in the naive Bayes formula?
The answer is yes, certainly, because the only message which contains a also contains b. (End of example.) The confusion arises because people usually don’t bother to write the denominator in the naive Bayes formula, as it doesn’t affect the calculations except for a scaling factor which is the same for spam as for not spam.