Data Analytics with Data Science Algorithm

Data Analytics with Data Science-
Naïve Bayes Classifier Algorithm
By
Dr. D.Shunmuga Kumari,
Assistant Professor,
Department of information Technology,

• Supervised learning is a machine learning method in which models are trained using labeled
data.
• Labeled data is data that has been tagged with a correct answer or classification.
• Once the training is completed, we will test the model by giving the new set of animal. The
model will identify the animal and predict the output using a suitable algorithm.

• Unsupervised learning is another machine learning method in which patterns inferred from
the unlabeled input data.
• The goal of unsupervised learning is to find the structure and patterns from the input data.
• Unsupervised learning does not need any supervision. Instead, it finds patterns from the
data by its own.

• Naïve Bayes algorithm is a supervised learning algorithm, which is based on
Bayes theorem and used for solving classification problems.
• It is mainly used in text classification that includes a high-dimensional
training dataset.
• Naïve Bayes Classifier is one of the simple and most effective Classification
algorithms which helps in building the fast machine learning models that can
make quick predictions.
• It is a probabilistic classifier, which means it predicts on the basis of the
probability of an object.
• Some popular examples of Naïve Bayes Algorithm are spam filtration,
Sentimental analysis, and classifying articles.

Why is it called Naïve Bayes?
• The Naïve Bayes algorithm is comprised of two words Naïve and Bayes,
• Which can be described as:
Naïve: It is called Naïve because it assumes that the occurrence of a certain feature is
independent of the occurrence of other features. Such as if the fruit is identified on the bases of
color, shape, and taste, then red, spherical, and sweet fruit is recognized as an apple.
• Hence each feature individually contributes to identify that it is an apple without depending
on each other.
Bayes: It is called Bayes because it depends on the principle of Bayes' Theorem.
Bayes' Theorem:
Bayes' theorem is also known as Bayes' Rule or Bayes' law, which is used to determine the
probability of a hypothesis with prior knowledge.
It depends on the conditional probability.

The formula for Bayes' theorem is given as:
Where,
 P(A|B) is Posterior probability: Probability of hypothesis A on the
observed event B.
 P(B|A) is Likelihood probability: Probability of the evidence given
that the probability of a hypothesis is true.
 P(A) is Prior Probability: Probability of hypothesis before
observing the evidence.
 P(B) is Marginal Probability: Probability of Evidence.

Working of Naïve Bayes' Classifier:
Working of Naïve Bayes' Classifier can be understood with the help of the below example:
Suppose we have a dataset of weather conditions and corresponding target variable "Play".
So using this dataset we need to decide that whether we should play or not on a particular
day according to the weather conditions.
So to solve this problem, we need to follow the below steps:
Convert the given dataset into frequency tables.
Generate Likelihood table by finding the probabilities of given features.
Now, use Bayes theorem to calculate the posterior probability.

Problem: If the weather is sunny, then the Player should play or not?
Solution: To solve this, first consider the below dataset:
Outlook Play
0 Rainy Yes
1 Sunny Yes
2 Overcast Yes
3 Overcast Yes
4 Sunny No
5 Rainy Yes
6 Sunny Yes
7 Overcast Yes
8 Rainy No
9 Sunny No
10 Sunny Yes
11 Rainy No
12 Overcast Yes
13 Overcast Yes

I. Frequency table for the Weather Conditions:
Weather Yes No
Overcast 5 0
Rainy 2 2
Sunny 3 2
Total 10 5

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II Likelihood table weather condition:
Weather No Yes
Overcast 0 5 5/14= 0.35
Rainy 2 2 4/14=0.29
Sunny 2 3 5/14=0.35
All 4/14=0.29 10/14=0.71

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Advantages of Naïve Bayes Classifier:
• Naïve Bayes is one of the fast and easy ML algorithms to predict a class of
datasets.
• It can be used for Binary as well as Multi-class Classifications.
• It performs well in Multi-class predictions as compared to the other Algorithms.
• It is the most popular choice for text classification problems.
Disadvantages of Naïve Bayes Classifier:
• Naive Bayes assumes that all features are independent or unrelated, so it cannot
learn the relationship between features.

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Applications of Naïve Bayes Classifier:
It is used for Credit Scoring.
It is used in medical data classification.
It can be used in real-time predictions because Naïve Bayes Classifier is an eager
learner.
It is used in Text classification such as Spam filtering and Sentiment analysis.

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