In this tutorial we will discuss about Naive Bayes text classifier. Naive Bayes is one of the simplest classifiers that one can use because of the simple mathematics that are involved and due to the fact that it is easy to code with every standard programming language including PHP, C#, JAVA etc. Update: The Datumbox Machine Learning Framework is now open-source and free to download. Note that some of the techniques described below are used on Datumbox's Text Analysis service and they power up our API. The Naive Bayes classifier is a simple probabilistic classifier which is based on Bayes theorem with strong and naïve independence assumptions. It is one of the most basic text classification techniques with various applications in email spam detection, personal email sorting, document categorization, sexually explicit content detection, language detection and sentiment detection.

With the Naive Bayes model, we do not take only a small set of positive and negative words into account, but all words the NB Classifier was trained with, i.e. all words presents in the training set. If a word has not appeared in the training set, we have no data available and apply Laplacian smoothing (use 1 instead of the conditional probability of the word). The probability a document belongs to a class C is given by the class probability P(C) multiplied by the products of the conditional probabilities of each word for that class. In theory we want a training set as large as possible, since that will increase the accuracy. Taking the n-th power of such a large number, will definitely result in computational problems, so we should normalize it.

A series of monte carlo studies were performed to assess the extent to which different inference procedures robustly output reasonable belief values in the context of increasing levels of judgmental imprecision. It was found that, when compared to an equal-weights linear model, the Bayesian procedures are more likely to deduce strong support for a hypothesis. But, the Bayesian procedures are also more likely to strongly support the wrong hypothesis. Bayesian techniques are more powerful, but are also more error prone.

Here's a situation you've got into: You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used'Naive Bayes', which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set.

You are working on a classification problem and you have generated your set of hypothesis, created features and discussed the importance of variables. Within an hour, stakeholders want to see the first cut of the model. You have hunderds of thousands of data points and quite a few variables in your training data set. In such situation, if I were at your place, I would have used'Naive Bayes', which can be extremely fast relative to other classification algorithms. It works on Bayes theorem of probability to predict the class of unknown data set.