How Naïve Bayes Algorithm works?
Naïve Bayes is a simple supervised machine learning algorithm that is used primarily for classification problems. It is one of the algorithms that are mainly used for text classification that has a high – dimensional training datasets. It is also the most effective classification algorithm which can be used in building fast machine learning models that makes quick predictions.
It works on the principle of prediction. Popular examples where this algorithm used are Spam filtration, classification of articles and Sentimental Analysis.
Working of Naïve Bayes Algorithm
Naïve Bayes Algorithm consists of two words: Naïve and Bayes, Naïve because it assumes occurrence of a certain feature is independent of occurrence of other features and Bayes because it depends on Bayes’ Theorem.
This algorithm is able to effectively solve many complex problems such as text classifiers than the much hyped neural networks. The model works well with insufficient and mislabelled data. Probability is a field of math that helps us to reason about uncertainty and calculates the likelihood of some events. When we work with a predictive machine learning model, such as Naïve Bayes Algorithm, we have to predict uncertain future.
Classification is the most used form of prediction. For binary classification, prediction results in classification to 0 or 1, such as spam or not spam. In case of multiclass classification, it aims to predict the class of record to wide variety of classes rather than 0 or 1.The Naïve Bayes machine learning algorithm is one of the methods to deal with uncertainty with the help of probabilistic methods.
When dealing with classification problem in supervised learning, we use labelled data where target class of the records is known. We use these data to train the model using these data and apply this particular trained model to new data where classification has to be done.
Advantages of Naïve Bayes Classifier are that it is fast and easy and can be used for binary as well as multiclass classification. It is the most foremost choice for text classification problems. One of the disadvantages is that it assumes that all features are independent or unrelated, so it cannot learn the relationship between the features.
The most important applications of the algorithm are Credit scoring, Medical data classification, real time predictions and Text classification such as Spam filtering and Sentimental Analysis.
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