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How does naive Bayes classify text?
Naive Bayes is a learning algorithm commonly applied to text classification. Some of the applications of the Naive Bayes classifier are: (Automatic) Classification of emails in folders, so incoming email messages go into folders such as: “Family”, “Friends”, “Updates”, “Promotions”, etc.
What does naive Bayes assume while classifying?
What is Naive Bayes algorithm? It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
Does naive Bayes can only be used for binary classification?
Classification Problems: Naive Bayes is a classification algorithm suitable for binary and multiclass classification.
When to use naive Bayes classifier?
Naive Bayes classifier is successfully used in various applications such as spam filtering, text classification, sentiment analysis, and recommender systems. It uses Bayes theorem of probability for prediction of unknown class.
Why is naive Bayesian classification called naive?
Naive Bayesian classification is called naive because it assumes class conditional independence. That is, the effect of an attribute value on a given class is independent of the values of the other attributes.
What is “naive” in a naive Bayes classifier?
The first assumption of a Naive Bayes classifier is that the value of a particular feature is independent of the value of any other feature. Which means that the interdependencies within data are comfortably neglected. Hence the name ‘naive.’
What is intuitive explanation of naive Bayes classifier?
Naive Bayes Classifier is a simple model that’s usually used in classification problems. The math behind it is quite easy to understand and the underlying principles are quite intuitive. Yet this model performs surprisingly well on many cases and this model and its variations are used in many problems.