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How are zero probabilities avoided in Naive Bayes?
One of the disadvantages of Naïve-Bayes is that if you have no occurrences of a class label and a certain attribute value together then the frequency-based probability estimate will be zero. And this will get a zero when all the probabilities are multiplied. This is how we’ll get rid of getting a zero probability.
What is the zero frequency problem?
If an individual class label is missing, then the frequency-based probability estimate will be zero. And we will get a zero when all the probabilities are multiplied.
How is Naive Bayes probability calculated?
The conditional probability can be calculated using the joint probability, although it would be intractable. Bayes Theorem provides a principled way for calculating the conditional probability. The simple form of the calculation for Bayes Theorem is as follows: P(A|B) = P(B|A) * P(A) / P(B)
What is non zero probability?
An event that has zero probability may be possible or impossible. However, if an event has a nonzero probability, it must be possible; that is, it cannot be impossible.
How to perform classification using naive Bayes classifier?
It performs well in case of categorical data as compared to numeric data. So, how do we perform classification using Naive Bayes when the data we have is continuous in nature.
How to solve the zero frequency problem in naive Bayes?
An approach to overcome this ‘zero-frequency problem’ is to add one to the count for every attribute value-class combination when an attribute value doesn’t occur with every class value. This will lead to the removal of all the zero values from the classes and, at the same time, will not impact the overall relative frequency of the classes.
When do you use Laplace estimator in Bayes classifier?
It occurs when any condition having zero probability in the whole multiplication of the likelihood makes the whole proabability zero. In such a case, there is something called Laplace Estimator is used.
Is the naive Bayes algorithm based on Bayes theorem?
B ackground Let first recall what is the Naive Bayes Algorithm. As the name suggests, it is based on the Bayes theorem of Probability and Statistics with a naive assumption that the features are independent of each other.