In order to make sense of data and to build machine learning systems, data is required to be classified, which is the process of predicting the class of data provided. There are many ways to classify data, and choosing the right classifier is the second step (first step is getting good data) to building machine learning systems. Think of classifiers as the category of data. One of the most widely used classifier is Naïve Bayes. In this post, we will look at the Naïve Bayes classifier in more detail.
A classifier utilizes training data to understand how a given input variables relates to the class. When the classifier is trained accurately, it can be used to identify other data that are similar.
Naïve Bayes classifiers sit in the family of “probabilistic classifiers”, which is the family of classifiers that are able to predict the probability of data, based on an input. It is liked due to its simplicity. Naïve Bayes classifiers assume that the data is independent of the value of all other data.
The benefit of the Naïve Bayes classifier comes from the fact it offers an efficient method to group data that is not biased by outliers. Naïve Bayes works really well with nonlinear problems, offering a probabilistic approach to classifying data. Think of its use in a production environment where you are predicting the probability that something will be produced that is defective.
What works against Naïve Bayes classifier is that it is based on the assumption that features have the same statistical relevance, but this is not always the case. This is the naïve component, where there are assumptions made about the independence in the data.
The naive Bayes classifier is surprisingly useful in its practice and a common method to classify data for machine learning purposes. It effectively provides the likelihood point from a set of data that will exhibit similar features to new data.
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