The document discusses the Naive Bayes classification model. It begins by explaining that a Naive Bayes classifier is a simple probabilistic classifier based on Bayes' theorem that makes strong independence assumptions. It assumes the presence or absence of a feature is unrelated to any other feature. The document then provides mathematical formulas to describe the Naive Bayes probabilistic model and explains how to apply it to classify data. An example is shown predicting whether someone will buy a computer based on attributes like age, income, student status, and credit rating. The document concludes by discussing some common applications of Naive Bayes classification like text classification, spam filtering, and recommender systems.

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ABHIJIT SENGUPTA
MBA(D),IV th Semester,
Roll no: 79
Naïve Bayes Classification Model
A Bayes classifier is a simple probabilistic classifier based on applying Bayes'
theorem (from Bayesian statistics) with strong (naive) independence assumptions.
A more descriptive term for the underlying probability model would be
"independent feature model".
In simple terms, a naive Bayes classifier assumes that the presence (or absence) of
a particular feature of a class is unrelated to the presence (or absence) of any
other feature. For example, a fruit may be considered to be an apple if it is red,
round, and about 4" in diameter. Even if these features depend on each other or
upon the existence of the other features, a naive Bayes classifier considers all of
these properties to independently contribute to the probability that this fruit is an
apple.
Probabilistic model
Abstractly, the probability model for a classifier is a conditional model
over a dependent class variable with a small number of outcomes or classes,
conditional on several feature variables through . The problem is that if the
number of features is large or when a feature can take on a large number of
values, then basing such a model on probability tables is infeasible. We therefore
reformulate the model to make it more tractable.
Using Bayes' theorem, this can be written
In plain English, using Bayesian Probability terminology, the above equation can
be written as

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In practice, there is interest only in the numerator of that fraction, because the
denominator does not depend on and the values of the features are given, so
that the denominator is effectively constant. The numerator is equivalent to the
joint probability model
which can be rewritten as follows, using the chain rule for repeated applications
of the definition of conditional probability:
Now the "naive" conditional independence assumptions come into play: assume
that each feature is conditionally independent of every other feature for
given the category . This means that
,
, ,
and so on, for . Thus, the joint model can be expressed as

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This means that under the above independence assumptions, the conditional
distribution over the class variable is:
where the evidence is a scaling factor dependent only on
, that is, a constant if the values of the feature variables are known.
The denominator Z is being constant, if we compare the numerators, we shall get
which of the outcome or class C is most likely to occur given a set of values of Fi.
Let us discuss this with the following example:
Example
In the following table ,there are four attributes age, income, student & credit
rating of 14 tuples.The records r1,r2,….r14 are described by their attributes. Let
us consider a tuple X having the following attributes :
X = ( age= youth, income = medium, student = yes, credit_rating = fair)
Will a person belonging to tuple X buy a computer?
Records AGE Income Student Credit
rating
Buys
Computer
R1 <=30 high No Fair No
R2 <=30 high No excellent No
R3 30-40 high No Fair Yes
R4 >40 medium No Fair Yes
R5 >40 low Yes Fair yes

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R6 >40 low Yes excellent No
R7 31-40 low Yes excellent Yes
R8 <=30 Medium No Fair no
R9 <=30 low Yes Fair Yes
R10 >40 medium Yes Fair Yes
R11 <=30 medium Yes excellent Yes
R12 30-40 medium No excellent Yes
R13 30-40 High Yes Fair Yes
R14 >40 medium no excellent no
D : Set of tuples
Each Tuple is an ‘n’ dimensional attribute vector X : (x1,x2,x3,…. xn)
Let there be ‘m’ Classes : C1,C2,C3…Cm
Naïve Bayes classifier predicts X belongs to Class Ci iff P (Ci/X) > P(Cj/X) for 1<= j
<= m , j <> i.
Maximum Posteriori Hypothesis: P(Ci/X) = P(X/Ci) P(Ci) / P(X)
Maximize P(X/Ci) P(Ci) as P(X) is constant
With many attributes, it is computationally expensive to evaluate P(X/Ci).
Naïve Assumption of “class conditional independence”
Now, P(X/Ci).P(Ci)= P(Xk/Ci).P(Ci)
& P(Xk/Ci) =P(X1/Ci).P(X2/Ci)……..P(Xn/Ci)
Theory applied on previous example:
P(C1) = P(buys_computer = yes) = 9/14 =0.643
P(C2) = P(buys_computer = no) = 5/14= 0.357
P(age=youth /buys_computer = yes) = 2/9 =0.222
P(age=youth /buys_computer = no) = 3/5 =0.600
P(income=medium /buys_computer = yes) = 4/9 =0.444
P(income=medium /buys_computer = no) = 2/5 =0.400
P(student=yes /buys_computer = yes) = 6/9 =0.667
P(credit rating=fair /buys_computer = no) = 2/5 =0.400

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P(X/Buys a computer = yes) = P(age=youth /buys_computer = yes) *
P(income=medium/buys_computer = yes) * P(student=yes /buys_computer = yes)
* P(credit rating=fair/buys_computer = yes)
= 0.222 * 0.444 * 0.667 * 0.667 = 0.044
Similliarly,P(X/Buys a computer = No) = 0.600 * 0.400 * 0.200 * 0.400 = 0.019
Now,find class Ci that Maximizes P(X/Ci) * P(Ci)
=>P(X/Buys a computer = yes) * P(buys_computer = yes) =.044*.643= 0.028
=>P(X/Buys a computer = No) * P(buys_computer = no) = .019*.357=0.007
Prediction : Buys a computer for Tuple X
In spite of their naive design and apparently over-simplified assumptions, naive
Bayes classifiers have worked quite well in many complex real-world situations. In
2004, analysis of the Bayesian classification problem has shown that there are
some theoretical reasons for the apparently unreasonable efficacy of naive Bayes
classifiers. Still, a comprehensive comparison with other classification methods in
2006 showed that Bayes classification is outperformed by more current
approaches, such as boosted trees or random forests.
An advantage of the naive Bayes classifier is that it requires a small amount of
training data to estimate the parameters (means and variances of the variables)
necessary for classification. Because independent variables are assumed, only the
variances of the variables for each class need to be determined and not the entire
covariance matrix. It is particularly suited when the dimensionality of the inputs is
high. Parameter estimation for naive Bayes models uses the method of maximum
likelihood. In spite over-simplified assumptions, it often performs better in many
complex real world situations.

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Uses of Naive Bayes classification:
1. Naive Bayes text classification
The Bayesian classification is used as a probabilistic learning method (Naive Bayes
text classification). Naive Bayes classifiers are among the most successful known
algorithms for learning to classify text documents.
2. Spam filtering
Spam filtering is the best known use of Naive Bayesian text classification. It makes
use of a naive Bayes classifier to identify spam e-mail.Bayesian spam filtering has
become a popular mechanism to distinguish illegitimate spam email from
legitimate email (sometimes called "ham" or "bacn").[4] Many modern mail
clients implement Bayesian spam filtering. Users can also install separate email
filtering programs.Server-side email filters, such as DSPAM, SpamAssassin,
SpamBayes, Bogofilter and ASSP, make use of Bayesian spam filtering techniques,
and the functionality is sometimes embedded within mail server software itself.
3. Hybrid Recommender System Using Naive Bayes Classifier and Collaborative
Filtering
Recommender Systems apply machine learning and data mining techniques for
filtering unseen information and can predict whether a user would like a given
resource.It is proposed a unique switching hybrid recommendation approach by
combining a Naïve Bayes classification approach with the collaborative filtering.
Experimental results on two different data sets show that the proposed algorithm
is scalable and provide better performance–in terms of accuracy and coverage–
than other algorithms while at the same time eliminates some recorded problems
with the recommender systems.
4. Online applications This online application has been set up as a simple example
of supervised machine learning and affective computing. Using a training set of
examples which reflect nice, nasty or neutral sentiments, we're training Ditto to
distinguish between them.Simple Emotion Modelling, combines a statistically
based classifier with a dynamical model. The Naive Bayes classifier employs single
words and word pairs as features. It allocates user utterances into nice, nasty and
neutral classes, labelled +1, -1 and 0 respectively. This numerical output drives a
simple first-order dynamical system, whose state represents the simulated
emotional state of the experiment's personification.

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Bibliography:
(http://nlp.stanford.edu/IR-book/html/htmledition/naive-bayes-text-
classification-1.html)
(http://en.wikipedia.org)
(http://eprints.ecs.soton.ac.uk/18483/)
(http://www.convo.co.uk/x02/)
http://www.statsoft.com
Data Mining Concepts & Techniques,Jiowai Han