How does Naïve Bayes compute the probability of an e mail belonging to a class spam not spam )?

According to this overview on Naïve Bayes spam filtering, 'Spamicity' can be calculated by taking the total number of emails that have already been hand-labelled as either spam or ham, and using that data to compute word spam probabilities, by counting the frequency of each word.

Can we use Naive Bayes for email spam detection?

Naive Bayes is a probabilistic algorithm based on the Bayes Theorem used for email spam filtering in data analytics. If you have an email account, we are sure that you have seen emails being categorised into different buckets and automatically being marked important, spam, promotions, etc.

How can we use Bayes rule in detection of spam mail?

The general formula for Bayes' Rule is: In this situation, the formula can be written as: For instance, the probability of the word “FREE” appears in an email is 20%, the probability of an email being a spam is 25%, and the probability of a junk email has the word “FREE” is 45%.

How is Naive Bayes probability calculated?

The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. The simple form of the calculation for Bayes Theorem is as follows: P(A|B) = P(B|A) * P(A) / P(B)

Why is Naive Bayes good for spam classification?

A bit of theory

Naive Bayes classification is a simple probability algorithm based on the fact, that all features of the model are independent. In the context of the spam filter, we suppose, that every word in the message is independent of all other words and we count them with the ignorance of the context.

Naive Bayes, Clearly Explained!!!

What is Bayes spam probability?

Bayesian spam filtering is based on Bayes rule, a statistical theorem that gives you the probability of an event. In Bayesian filtering it is used to give you the probability that a certain email is spam.

What does naive Bayes classifier do?

Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object.

How do you use Naive Bayes for classification?

What is the method that calculates the probability of a new data entering any of the existing classes using existing classified data?

The technique is easiest to understand when described using binary or categorical input values. It is called naive Bayes or idiot Bayes because the calculation of the probabilities for each hypothesis are simplified to make their calculation tractable.

How is naive Bayes algorithm implemented?

Which algorithm is used for spam detection?

Algorithm Implementation

Naive Bayes is a simple and a probabilistic traditional machine learning algorithm. It is very popular even in the past in solving problems like spam detection.

What is the probability that a message is spam given that it contains the word free?

Expert-verified answer

3.57% of all messages contain the word “free” and are marked as spam. To Find: the probability that a message contains the word “free”, given that it is spam.

What is the prior probability of a mail being spam?

The prior is 20%, the probability that an email is spam, knowing nothing about the email. The event is that the email contains the word 'lottery'.

How do spam filters use classification algorithm?

Adaptive Spam Filtering Technique

Algorithms classify the incoming mails into various groups and, based on the comparison scores of every group with the defined set of groups, spam, and non-spam emails got segregated.

How do spam algorithms work?

When you mark a message as spam, it goes into a hopper with millions of messages that others have flagged. Algorithms churn through these messages to find similar characteristics, such as word proximity or misspellings, that show up frequently in spam.

What type of probability is the probability that an email contains the word Viagra and it is spam?

For example, if an email contains the word Viagra, we classify it as 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 type of naïve Bayes algorithm will be used to build the predictive model with frequency count feature?

Multinomial Naive Bayes

This is used when the features represent the frequency. Suppose you have a text document and you extract all the unique words and create multiple features where each feature represents the count of the word in the document. In such a case, we have a frequency as a feature.

What is naive Bayes algorithm in data mining?

The Naive Bayes classification algorithm is a probabilistic classifier. It is based on probability models that incorporate strong independence assumptions. The independence assumptions often do not have an impact on reality. Therefore they are considered as naive.

How does Gaussian Naive Bayes classifier work?

Gaussian Naive Bayes supports continuous valued features and models each as conforming to a Gaussian (normal) distribution. An approach to create a simple model is to assume that the data is described by a Gaussian distribution with no co-variance (independent dimensions) between dimensions.

How does the posterior probability of a class is computed by naive Bayes classifier?

Bayes theorem provides a way of calculating the posterior probability, P(c|x), from P(c), P(x), and P(x|c). Naive Bayes classifier assume that the effect of the value of a predictor (x) on a given class (c) is independent of the values of other predictors. This assumption is called class conditional independence.

How is naïve Bayes algorithm useful for learning and classifying text?

Since a Naive Bayes text classifier is based on the Bayes's Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.

How Bayes Theorem is used for classification?

Bayesian classification uses Bayes theorem to predict the occurrence of any event. Bayesian classifiers are the statistical classifiers with the Bayesian probability understandings. The theory expresses how a level of belief, expressed as a probability.

Why naïve Bayesian classification is called naïve?

Naïve Bayes classification is called Naïve because it assumes class conditional independence. The effect of an attribute value on a given class is independent of the values of the other attributes. This assumption is made to reduce computational costs and hence is considered Naïve.

How does Bayesian spam filter work?

A Bayesian filter works by comparing your incoming email with a database of emails, which are categorised into 'spam' and 'not spam'. Bayes' theorem is used to learn from these prior messages. Then, the filter can calculate a spam probability score against each new message entering your inbox.

Why do you think that Naive Bayes is a good choice when it comes to text classification problems such as spam detection?

When you actually get to work with the above algorithms, Naive Bayes gives you the best kind of results which are desired. In applications like spam filtering and sentiment analysis, the data majorly consists of the textual data in the form of reviews or the contents of an email.