What are the disadvantages of using a naive Bayes for classification?
Disadvantages of Naive Bayes The main limitation of Naive Bayes is the assumption of independent predictor features. If a categorical variable has a category in the test dataset, which was not observed in training dataset, then the model will assign a 0 (zero) probability and will be unable to make a prediction.
Why does Naive Bayes work well with text classification?
Since a Naive Bayes text classifier is based on the Bayes’s Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful.
Why does Naive Bayes work for text classification?
Naive Bayesian algorithm is a simple classification algorithm which uses probability of the events for its purpose. It is based on the Bayes Theorem which assumes that there is no interdependence amongst the variables. Calculating these probabilities will help us calculate probabilities of the words in the text.
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.