In this article, we are going to understand the concept of the Naive Bayes Classifier. In a world loaded with AI and machine learning encompassing nearly everything around us, the most important part of machine learning is the classification of data, and the prediction of the output of data.
Naive Bayes Classifier is a straightforward but surprisingly ground-breaking algorithm for predicting the outcomes of data set and categorizing the text according to its content. The mathematical concept that we use in the Naive Bayes Classifier is Bayes’ Theorem.
Well, we will understand the concept of Bayes’ Theorem later. Naive Bayes Classifier is anything but difficult to construct and especially valuable for huge amounts of data.
The Naive Bayes classifier assumes that the presence of certain characteristics of a data is autonomous of different characteristics. Suppose that, we have been given data that contains all the names of the animals and we have to filter all the dogs from the data.
An animal can be viewed as a dog by the fact that it has four legs, fur and it has a tail. But the Naive Bayes classifier will classify a dog to every animal that has four legs, fur or tail, for instance, a cat or a squirrel.
Naive Bayes doesn’t take into consideration the relationships between characteristics, we all know that just because an animal has four legs, fur or tail does not mean that it is a dog, that is the reason it is called ‘Naive’. Well, ‘Bayes’ in Naive Bayes Classifier is Bayes’ Theorem.
In general, this theorem is used to figure out the conditional probability. Basically, conditional probability is the probability of an event occurring, only after it has some relationship to one or more other events.
For example, we park our cars in the parking lot of a mall then, the probability of getting a parking space depends on the time of day we park, where we park, and what conventions are going on at that time. Let’s take a real-life example to understand the concept of Bayes’ Theorem.
Suppose that you are working in your kitchen and you hear your phone ringing. Generally, you keep your phone on bed or your table. You went to your room and check both the locations but you didn’t find your phone.
Then, you combine your prior knowledge of the phone (that you usually keep it on your bed or your table) with the new evidence (that it can be anywhere in the house) to find its location. You use your prior knowledge of where you sometimes left your phone and narrow down the search.
You will ignore most places in the house and consider most likely places (like your drawer or on the charging) until you eventually find your phone. In this way, you have used Bayes’ theorem to find your phone.
What are the applications of the Naive Bayes Classifier?
Here, are a couple of applications of Naive Bayes Classifier –
- We use the Naive Bayes Classifier in news categorization. Each news site has its different layout and categories for grouping news. Each news article is categorized by removing the less significant word from the article.
- It is a well-known technique that is utilized in email filtering. They utilize a group of words to distinguish spam email, a methodology ordinarily utilized in text classification. Naive Bayes classifiers work by sifting through the utilization of words with spam and non-spam messages and afterwards utilizing Bayes’ theorem to ascertain a likelihood that an email is or isn’t spam.
- These days, modern hospitals are well furnished with monitoring and other information assortment gadgets bringing about huge information that are gathered persistently through wellbeing assessment and clinical treatment. One of the principal focal points of the Naive Bayes approach is that it helps the way how doctors analyze patients.
- Weather is one of the most persuasive factors in our day by day life, to the degree that it might influence the economy of a nation that relies upon occupation like agriculture. Weather prediction has been a difficult issue in the meteorological department for quite a long time.
- Significantly after the innovative and logical progression, the precision in the prediction of weather has never been adequate. Naive Bayes Classifier is additionally utilized for weather prediction by considering all the past meteorological forecasts at that point to classify every one of them. Afterwards, applying Bayes theorem on it we get the ideal outcome.