naive bayes classifier | Introduction to Naive Bayes Theorem | Machine Learning Algorithm (2019)

#Naivebayesclassifier #MachineLearning #CodeWrestling
This video explains the concept of classification of text from a set of documents using a Naive Bayes Classifier approach.
This video also deals with the concept of Bayes Theorem.

We have explained the topic using a sample dataset of text which is classified as of whether it belongs to "sports" category or not.

We train the model and then classify a new sentence 'A very close game' by finding its probability for belonging to "sports" category or not. The most likely probability is the final category, that sentence belongs to.

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. Naive Bayes classifier is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. Naive Bayes is not only known for its simplicity, but also for its effectiveness. Naive Bayes is fast to build models and make predictions with the Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving a text classification problem. Hence, you should learn this algorithm thoroughly.

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