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.

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.

In my last blog, I discussed k-Nearest Neighbor machine learning algorithms with an example that was hopefully easy to understand for beginners. During the summer of 2017 I began a five-part series on types of machine learning. That series included more details about K-means clustering, Singular Value Decomposition, Principal Component Analysis, Apriori and Frequent Pattern-Growth. Today I want to expand on the ideas presented in my Naive Bayes "Data Science in 90 Seconds" You Tube video and continue the discussion in plain language.

In this Course you learn k-Nearest Neighbors & Naive Bayes Classification Methods. In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. The k-NN algorithm is among the simplest of all machine learning algorithms. For classification, a useful technique can be to assign weight to the contributions of the neighbors, so that the nearer neighbors contribute more to the average than the more distant ones. The neighbors are taken from a set of objects for which the class (for k-NN classification).