Which naive bayes

Secondly, each feature is given the same weight or importance. None of the attributes is irrelevant and assumed to be contributing equally to the outcome. Note: The assumptions made by Naive Bayes are not generally correct in real-world situations. In-fact, the independence assumption is never correct but often works well in practice.

Basically, we are trying to find probability of event A, given the event B is true. Event B is also termed as evidence. P A is the priori of A the prior probability, i. Probability of event before evidence is seen. The evidence is an attribute value of an unknown instance here, it is event B. P A B is a posteriori probability of B, i. So now, we split evidence into the independent parts. Now, we need to create a classifier model. For this, we find the probability of given set of inputs for all possible values of the class variable y and pick up the output with maximum probability.

This can be expressed mathematically as: So, finally, we are left with the task of calculating P y and P x i y. Please note that P y is also called class probability and P x i y is called conditional probability. The different naive Bayes classifiers differ mainly by the assumptions they make regarding the distribution of P x i y.

Manning, P. Raghavan and H. Introduction to Information Retrieval. Cambridge University Press, pp. McCallum and K. Nigam A comparison of event models for Naive Bayes text classification. Metsis, I. Androutsopoulos and G. Paliouras CategoricalNB implements the categorical naive Bayes algorithm for categorically distributed data. Naive Bayes models can be used to tackle large scale classification problems for which the full training set might not fit in memory.

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Importing the libraries import numpy as nm import matplotlib. Fitting Naive Bayes to the Training set from sklearn. Making the Confusion Matrix from sklearn. Visualising the Training set results from matplotlib. Visualising the Test set results from matplotlib. Next Topic Classification vs Regression. Reinforcement Learning. R Programming. React Native. Python Design Patterns. Python Pillow. Python Turtle.