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How does naive Bayes work on continuous variables?
There are two ways to estimate the class-conditional probabilities for continuous attributes in naive Bayes classifiers: We can discretize each continuous attribute and then replace the continuous attribute value with its corresponding discrete interval.
Does naive Bayes work with continuous data?
Continuous Data For continuous features, there are essentially two choices: discretization and continuous Naive Bayes. Discretization works by breaking the data into categorical values.
Which naive Bayes model will you use for data set containing continuous feature values?
Gaussian Naïve Bayes Classifier: In Gaussian Naïve Bayes, continuous values associated with each feature are assumed to be distributed according to a Gaussian distribution (Normal distribution).
What do you mean by Bayes Theorem?
Bayes’ theorem, named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability. Bayes’ theorem provides a way to revise existing predictions or theories (update probabilities) given new or additional evidence.
What makes naive Bayes classification so naive?
Naive Bayes is so ‘naive’ because it makes assumptions that are virtually impossible to see in real-life data and assumes that all the features are independent. Let’s take an example and implement the Naive Bayes Classifier, here we have a dataset that has been given to us and we’ve got a scatterplot which represents it.
What is the naive Bayes algorithm used for?
Naive Bayes is a probabilistic machine learning algorithm designed to accomplish classification tasks. It is currently being used in varieties of tasks such as sentiment prediction analysis, spam filtering and classification of documents etc.
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.
Why is naive Bayes 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.