Naïve Bayes Classifiers
What you learn in Naïve Bayes Classifiers ?
About this Free Certificate Course
Machine learning is a domain that is super trending in recent years. This is because of the fact that machine learning has multiple algorithms that have the ability to learn very well and do tasks at a lightning pace. This advantage that the domain has is because of algorithms such as Naive Bayes. It is a supervised learning algorithm that is very popularly used in the domain of social media analytics and more. Naive Bayes is seeing a huge uptrend in recent days and the requirement for proficient ML engineers is rising. Since it is this important to know the in and out of Naive Bayes classifiers, we here at Great Learning have come up with this course to help you understand all of the foundational concepts completely and put them into practice.
Course Outline
Data is the soul of Machine Learning, and there are specific methods to deal with it efficiently. This module first introduces Machine Learning and talks about the mathematical procedures involved. You will learn about supervised and unsupervised learning, Data Science Machine Learning steps, linear regression, Pearson's coefficient, best fit line, and coefficient of determinant. Lastly, you will be going through a case study to help you effectively comprehend Machine Learning concepts.
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Frequently Asked Questions
Why is it named Naïve Bayes?
The Naive Bayes algorithm is made up of two words, i.e., Naïve and Bayes. The word 'Naïve' is used because NB (Naive Bayes) assumes that the occurrence of a certain feature is independent of the occurrence of other features. For example, for the problem of classifying a tweet as safe or profane, we use features such as cuss words, foul expressions, and negative words, the presence of one feature is completely independent of the other. Moreover, each feature set will have a specific and deterministic distribution that shall be different from any other feature. This makes each feature contribute individually to identify the class of a tweet without depending on any other feature. The word 'Bayes' comes from the fact that NB is based on the principle of Bayes' Theorem.
What is Naïve Bayes classifier algorithm?
It is a supervised learning classifier that is based on classic probability’s Bayes theorem.
What is Naïve Bayes classifier and how does it work?
Naive Bayes or NB is a class supervised learning classifier that is based on the application of Classical Probability's Bayes Theorem. It predicts membership probabilities for each class in the set based on the probability that a given record or data point belongs to a particular class. The class with the highest probability is classified as the most likely class for that item. This is also known as Maximum A Posteriori or MAP.
What do you mean by MAP ?
MAP or Maximum A Posteriori probability is the highest probability among given classes, and this determines which class an observation belongs to.
Suppose H is our hypothesis and E is given Evidence. The MAP for a hypothesis H would be given as follows:
MAP(H) = max( P(H|E) )
==> MAP(H) = max( (P(E|H)*P(H))/P(E))
==> MAP(H) = max(P(E|H)*P(H)) (Since P(E)=1 for entire evidence set)
What are the two assumptions taken by Naïve Bayes algorithm?
Naive Bayes classifier assumes that all the features are totally unrelated to each other, i.e., the influence of one feature is totally independent of the influence of the other feature. Secondly, NB assumes that a feature set is identically distributive.
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