How the Naive Bayes Classifier works in Machine Learning
Naive Bayes classifier is a straightforward and powerful algorithm for the classification task. Even if we are working on a data set with millions of records with some attributes, it is suggested to try Naive Bayes approach.
Naive Bayes classifier gives great results when we use it for textual data analysis. Such as Natural Language Processing.
To understand the naive Bayes classifier we need to understand the Bayes theorem. So let’s first discuss the Bayes Theorem.
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What is Bayes Theorem?
Bayes theorem named after Rev. Thomas Bayes. It works on conditional probability. Conditional probability is the probability that something will happen, given that something else has already occurred. Using the conditional probability, we can calculate the probability of an event using its prior knowledge.
Below is the formula for calculating the conditional probability.
where
 P(H) is the probability of hypothesis H being true. This is known as the prior probability.
 P(E) is the probability of the evidence(regardless of the hypothesis).
 P(EH) is the probability of the evidence given that hypothesis is true.
 P(HE) is the probability of the hypothesis given that the evidence is there.
Let’s consider an example to understand how the above formula of Bayes theorem works.
Problem:
A Path Lab is performing a Test of disease say “D” with two results “Positive” & “Negative.” They guarantee that their test result is 99% accurate: if you have the disease, they will give test positive 99% of the time. If you don’t have the disease, they will test negative 99% of the time. If 3% of all the people have this disease and test gives “positive” result, what is the probability that you actually have the disease?
For solving the above problem, we will have to use conditional probability.
Probability of people suffering from Disease D, P(D) = 0.03 = 3%
Probability that test gives “positive” result and patient have the disease, P(Pos  D) = 0.99 =99%
Probability of people not suffering from Disease D, P(~D) = 0.97 = 97%
Probability that test gives “positive” result and patient does have the disease, P(Pos  ~D) = 0.01 =1%
For calculating the probability that the patient actually have the disease i.e, P( D  Pos) we will use Bayes theorem:
We have all the values of numerator but we need to calculate P(Pos):
P(Pos) = P(D, pos) + P( ~D, pos)
= P(posD)*P(D) + P(pos~D)*P(~D)
= 0.99 * 0.03 + 0.01 * 0.97
= 0.0297 + 0.0097
= 0.0394
Let’s calculate, P( D  Pos) = (P(Pos  D) * P(D)) / P(Pos)
= (0.99 * 0.03) / 0.0394
= 0.753807107
So, Approximately 75% chances are there that the patient is actually suffering from disease.
I hope we understand the Bayes theorem. Now let’s use this understanding to find out more about the naive Bayes classifier.
Naive Bayes Classifier
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class. The class with the highest probability is considered as the most likely class. This is also known as Maximum A Posteriori (MAP).
The MAP for a hypothesis is:
MAP(H)
= max( P(HE) )
= max( (P(EH)*P(H))/P(E))
= max(P(EH)*P(H))
P(E) is evidence probability, and it is used to normalize the result. It remains same so, removing it won’t affect.
Naive Bayes classifier assumes that all the features are unrelated to each other. Presence or absence of a feature does not influence the presence or absence of any other feature. We can use Wikipedia example for explaining the logic i.e.,
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.
In real datasets, we test a hypothesis given multiple evidence(feature). So, calculations become complicated. To simplify the work, the feature independence approach is used to ‘uncouple’ multiple evidence and treat each as an independent one.
P(HMultiple Evidences) = P(E1 H)* P(E2H) ……*P(EnH) * P(H) / P(Multiple Evidences)
Example of Naive Bayes Classifier
For understanding a theoretical concept, the best procedure is to try it on an example. Since I am a pet lover so selected animals as our predicted class. 🙂
Let’s consider a training dataset with 1500 records and 3 classes. We presume that there are no missing values in our data. We have
We have 3 classes associated with Animal Types:
 Parrot,
 Dog,
 Fish.
The Predictor features set consists of 4 features as
 Swim
 Wings
 Green Color
 Dangerous Teeth.
Green Color, Dangerous Teeth. All the features are categorical variables with either of the 2 values: T(True) or F( False).
Swim  Wings  Green Color  Dangerous Teeth  Animal Type 
50  500/500  400/500  0  Parrot 
450/500  0  0  500/500  Dog 
500/500  0  100/500  50/500  Fish 
The above table shows a frequency table of our data. In our training data:
 Parrots have 50(10%) value for Swim, i.e., 10% parrot can swim according to our data, 500 out of 500(100%) parrots have wings, 400 out of 500(80%) parrots are Green and 0(0%) parrots have Dangerous Teeth.
 Classes with Animal type Dogs shows that 450 out of 500(90%) can swim, 0(0%) dogs have wings, 0(0%) dogs are of Green color and 500 out of 500(100%) dogs have Dangerous Teeth.
 Classes with Animal type Fishes shows that 500 out of 500(100%) can swim, 0(0%) fishes have wings, 100(20%) fishes are of Green color and 50 out of 500(10%) dogs have Dangerous Teeth.
Now, it’s time to work on predict classes using the Naive Bayes model. We have taken 2 records that have values in their feature set, but the target variable needs to predicted.
Swim  Wings  Green  Teeth  
1.  True  False  True  False 
2.  True  False  True  True 
We have to predict animal type using the feature values. We have to predict whether the animal is a Dog, a Parrot or a Fish
We will use the Naive Bayes approach
P(HMultiple Evidences) = P(E1 H)* P(E2H) ……*P(EnH) * P(H) / P(Multiple Evidences)
Let’s consider the first record.
The Evidence here is Swim & Green. The Hypothesis can be an animal type to be Dog, Parrot, Fish.
For Hypothesis testing for the animal to be a Dog:
P(Dog  Swim, Green) = P(SwimDog) * P(GreenDog) * P(Dog) / P(Swim, Green)
= 0.9 * 0 * 0.333 / P(Swim, Green)
= 0
For Hypothesis testing for the animal to be a Parrot:
P(Parrot Swim, Green) = P(SwimParrot) * P(GreenParrot) * P(Parrot) / P(Swim, Green)
= 0.1 * 0.80 * 0.333 / P(Swim, Green)
= 0.0264/ P(Swim, Green)
For Hypothesis testing for the animal to be a Fish:
P(Fish Swim, Green) = P(SwimFish) * P(GreenFish) * P(Fish) / P(Swim, Green)
= 1 * 0.2 * 0.333 / P(Swim, Green)
= 0.0666/ P(Swim, Green)
The denominator of all the above calculations is same i.e, P(Swim, Green). The value of P(Fish Swim, Green) is greater that P(Parrot Swim, Green).
Using Naive Bayes, we can predict that the class of this record is Fish.
Let’s consider the second record.
The Evidence here is Swim, Green & Teeth. The Hypothesis can be an animal type to be Dog, Parrot, Fish.
For Hypothesis testing for the animal to be a Dog:
P(Dog  Swim, Green, Teeth) = P(SwimDog) * P(GreenDog) * P(TeethDog) * P(Dog) / P(Swim, Green, Teeth)
= 0.9 * 0 * 1 * 0.333 / P(Swim, Green, Teeth)
= 0
For Hypothesis testing for the animal to be a Parrot:
P(Parrot Swim, Green, Teeth) = P(SwimParrot) * P(GreenParrot)* P(TeethParrot) * P(Parrot) / P(Swim, Green, Teeth)
= 0.1 * 0.80 * 0 *0.333 / P(Swim, Green, Teeth)
= 0
For Hypothesis testing for the animal to be a Fish:
P(FishSwim, Green, Teeth) = P(SwimFish) * P(GreenFish) * P(TeethFish) *P(Fish) / P(Swim, Green, Teeth)
= 1 * 0.2 * 0.1 * 0.333 / P(Swim, Green, Teeth)
= 0.00666 / P(Swim, Green, Teeth)
The denominator of all the above calculations is same i.e, P(Swim, Green, Teeth). The value of P(Fish Swim, Green, Teeth) is the only positive value greater than 0. Using Naive Bayes, we can predict that the class of this record is Fish.
As the calculated value of probabilities is very less. To normalize these values, we need to use denominators.
Let’s proceed to learn the various type of Naive Bayes Methods.
Types of Naive Bayes Algorithm
Gaussian Naive Bayes
When attribute values are continuous, an assumption is made that the values associated with each class are distributed according to Gaussian i.e., Normal Distribution.
If in our data, an attribute say “x” contains continuous data. We first segment the data by the class and then compute mean & Variance of each class.
MultiNomial Naive Bayes
MultiNomial Naive Bayes is preferred to use on data that is multinomially distributed. It is one of the standard classic algorithms. Which is used in text categorization (classification). Each event in text classification represents the occurrence of a word in a document.
Bernoulli Naive Bayes
Bernoulli Naive Bayes is used on the data that is distributed according to multivariate Bernoulli distributions.i.e., multiple features can be there, but each one is assumed to be a binaryvalued (Bernoulli, boolean) variable. So, it requires features to be binary valued.
Advantages and Disadvantage of Naive Bayes classifier
Advantages
 Naive Bayes Algorithm is a fast, highly scalable algorithm.
 Naive Bayes can be use for Binary and Multiclass classification. It provides different types of Naive Bayes Algorithms like GaussianNB, MultinomialNB, BernoulliNB.
 It is a simple algorithm that depends on doing a bunch of counts.
 Great choice for Text Classification problems. It’s a popular choice for spam email classification.
 It can be easily train on small dataset
Disadvantages
 It considers all the features to be unrelated, so it cannot learn the relationship between features. E.g., Let’s say Remo is going to a part. While cloth selection for the party, Remo is looking at his cupboard. Remo likes to wear a white color shirt. In Jeans, he likes to wear a brown Jeans, But Remo doesn’t like wearing a white shirt with Brown Jeans. Naive Bayes can learn individual features importance but can’t determine the relationship among features.
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The most simplest and easiest explanation i came across . Brilliant work
Hi Nitesh,
Thanks for your compliment. 🙂