Chandana. S
5th SEM BCA
Naïve bayes classifier is
the collection of classification algorithms based on bayes theorem. It is the
family of algorithm where a value of a particular feature in a class is
unrelated to the value of any other feature. The statistician and philosopher,
Thomas bayes and the theorem named after him, bayes ’theorem, which is the base
for naïve bayes algorithm. This algorithm or model is easy to build and used
for very large data sets. Naïve bayes is also known to highly sophisticated
classification methods. We also use machine learning, python, R etc. languages in
this model. P(c|x) from p(c), p(x) and p(x|c) formulae is used to calculate naïve
bayes algorithm.
P(c|x)=P(x|c)p(c)/p(x)
Naïve bayes algorithm is used
to solve the probabilistic queries in different class based on various
attributes. Naïve bayes algorithm is a real time predictor which is a fast
learning algorithm used to make predictions in real time. It is also used for
binary classification and multiclass classification. It has three types of
algorithm called GaussianNB, multinomialNB, BernoulliNB. The main applications
of bayes algorithm is real time prediction, multiclass classification, text
classification. This algorithm is also used for spam messages or spam mails.
Examples: To mark an email
as spam, or not spam.
Classify a news article about
technology, politics, or sports.
This is also used for face
recognition algorithm software.
The main limitation of naïve
bayes algorithm is the considering of independent predictor features.
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