How does Bayes theorem classify text?

How does Bayes theorem classify text?

2. The Naive Bayes algorithm

1. Naive Bayes classifiers are a collection of classification algorithms based on Bayes’ Theorem.
2. The dataset is divided into two parts, namely, feature matrix and the response/target vector.
3. Naive Bayes assumes that each feature/variable of the same class makes an:
4. contribution to the outcome.

Why Naive Bayes is used in NLP?

Naive Bayes are mostly used in natural language processing (NLP) problems. Naive Bayes predict the tag of a text. They calculate the probability of each tag for a given text and then output the tag with the highest one.

Can we use Naive Bayes for text classification?

Naive Bayes is a learning algorithm commonly applied to text classification. Some of the applications of the Naive Bayes classifier are: (Automatic) Classification of emails in folders, so incoming email messages go into folders such as: “Family”, “Friends”, “Updates”, “Promotions”, etc.

Can we use naive Bayes for text classification?

Can you use naive Bayes for text classification?

Text Classification Using Naive Bayes There are many different machine learning algorithms we can choose from when doing text classification with machine learning. One family of those algorithms is known as Naive Bayes (NB) which can provide accurate results without much training data.

Which is the objective function in naive Bayes?

The objective function in the naive Bayes probability is to maximize the posterior probability given the training data in order to formulate the decision rule. To continue with our example above, we can formulate the decision rule based on the posterior probabilities as follows:

When does equation become zero in naive Bayes?

Since the above equation involves probabilities of each word of a new sentence with respect to a class, if a word from the new sentence does not occur in the class within the training set, the equation becomes zero. eg: the word ‘price’ did not occur in our training set in either of the classes.

Which is the probabilistic model of naive Bayes classifiers?

Figure 1. Naive Bayes classifiers are linear classifiers that are known for being simple yet very efficient. The probabilistic model of naive Bayes classifiers is based on Bayes’ theorem, and the adjective naive comes from the assumption that the features in a dataset are mutually independent.