#### World's Best AI Learning Platform with profoundly Demanding Certification Programs

Designed by IITian's, only for AI Learners.

Download our e-book of Introduction To Python

How to leave/exit/deactivate a Python virtualenvironment Exception Type: JSONDecodeError at /update/ Exception Value: Expecting value: line 1 column 1 (char 0) How to extracting text from PDF file using python What is Ensemble Learning? Which are different modes to open a file ? How to integrate Sales force and Django? How to Unpacking dictionaries using the ** operator? How to plot Bubble plot with Encircling? Join Discussion

4 (4,001 Ratings)

218 Learners

Dec 4th (7:00 PM) 213 Registered

Shashank Shanu

a year ago

Naive Bayes Algorithm

Imagine you are a data scientist and
your manager told you to build a classification model. You collected all the
required data and started working on it. Your data was very large with millions
of data points. Suddenly your manager came to you and told you that your
deadline for the project is reduced and you have to submit it in 2 days.

At this
situation, what will you do? You have a very large amount of data with very
less features in your data set.

In that case
if I had to make such a model I would have used ‘*Naive Bayes*’,
as Naïve bayes is considered to be one of a fastest algorithm when it comes for
classification tasks.

So, in
this article, I will try to give an in-depth explanation of Naïve bayes
algorithm, its types, how we can implement it into python, some of the pro’s
and cons of this algorithm.

So, let’s
start…

Naive Bayes is one of the mostly used machine learning model that
is used when we are having large volumes of data, even if you are working with
data that has millions of data records the recommended approach is Naive Bayes.
It gives very good results while performing sentimental analysis. It is a fast
and uncomplicated classification algorithm.

This model is mainly used for classification tasks and the model
is based on **“Bayes Theorem**”. This algorithm makes an assumption of independence
among predictors.

Let’s me give you some of the assumptions made by Naïve bayes
algorithm.

- It assumes that the presence of a particular features in a class is not dependent to the presence of the any other features in the dataset.

- Each features or independent variables are given same weight or importance.

Let me given you an example which helps you to understand it
better.

Even if all these features depend on each other or the others
feature’s, all of these properties independently contribute to the probability
that this fruit is an orange and that is why it is called as **“Naïve”**
which implies **lack of wisdom or judgement. **

Naïve Bayes algorithm is very easy to build and very useful when
we have large datasets. Along with its simplicity, Naïve Bayes is known to
outperform even highly sophisticated classification models.

Now as we get some idea about Naïve Bayes algorithm so to
understand the naive Bayes classifier in a better way, first we need to
understand the Bayes theorem. So, let’s first understand the Bayes Theorem.

Bayes Theorem works on conditional probability. Conditional
probability is the probability that something will happen, given that something
else has already happened/occurred. It helps us in calculating the probability
of an event using its prior knowledge. The formulae can be given as:

Conditional probability:

Let’s me give you, what each symbol represents in the formulae:

**P(E|H):**It represents the probability of the evidence given that hypothesis is true.

**P(H|E):**It represents the probability of the hypothesis given that the evidence is true.

**P(H):**It represents the probability of hypothesis H being true. This is known as the prior probability.

**P(E):**It represents the probability of the evidence.

As of now, you understood what is Bayes theorem. Now, let move
forward and understand with an example of how naïve Bayes algorithm works.

Let’s
suppose, we have a
training data set of weather and their corresponding target variable ‘Play’
(suggesting possibilities of playing). Our task is to classify whether players
will play or not based on weather condition.

Likelihood table

Problem: Players will play if weather is sunny. Is
this statement is correct?

We can solve this problem
using the above method of the posterior probability.

P (Yes | Sunny) = P (Sunny |
Yes) * P(Yes) / P (Sunny)

Here, we have P (Sunny |Yes)
= 3/9 = 0.33, P(Sunny) = 5/14 = 0.36, P(Yes)= 9/14 = 0.64

Now, P (Yes | Sunny) = 0.33 *
0.64 / 0.36 = 0.60, which has higher probability.

There is 60% change that
players will play when the weather is Sunny.

Therefore, predicted class is
**“Yes”.**

Naive Bayes also uses a similar
method to predict the probability of different class based on various
attributes. This algorithm is mostly used in text classification and with
problems having multiple classes.

Based on the data types of
our variables, features or predictor, we may need to use a particular types of
Naïve Bayes algorithm.

It is used when our features
are “Binary/Boolean” such that they take only two values likes 0 or 1, Yes or
No, or true or false.

It is used when the features
are of “discrete in nature”.

For example, it is used in
text analysis, where we take the count of each word in given documents and try
to predict the class/label.

It is used when the
predictors are of continuous data type, while using gaussian naïve bayes make
an assumption that they follow normal distribution or Gaussian distribution.

- Naïve Bayes is a highly extensible algorithm which is very fast.

- It can be used for both binaries as well as multiclass classification.
- It has mainly three different types of algorithms that are GaussianNB, MultinomialNB, BernoulliNB.
- It is a famous algorithm for spam email classification.
- It can be easily trained on small datasets and can be used for large volumes of data as well.

- The main disadvantage of the NB is considering all the variables independent that contributes to the probability.

- Real time Prediction: Being a fast learning algorithm it can be used to make predictions in real-time as well.

- Multiclass Classification: It can be used for multi-class classification problems also.

- Text Classification: As it has shown good results in predicting multi-class classification so it has more success rates compared to all other algorithms. As a result, it is majorly used in sentiment analysis & spam detection.

As of now, we get a clear
picture about what is naïve bayes? How its works? And the types of Naïve Bayes.
Let see how we can implement it in python.

In this example, we will be
using iris dataset which is available in the python.

```
#Importing Libraries
import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix,classification_report,
accuracy_score
#Loading Datasets
dataset = datasets.load_iris()
#Creating Our Naive Bayes Model Using Sckit-learn
gnb = GaussianNB()
gnb.fit(dataset.data, dataset.target)
#Making Predictions
expected = dataset.target
predicted = gnb.predict(dataset.data)
```

```
#Getting Accuracy
acc = accuracy_score(expected,predicted)
print("Accuracy of the model: ", acc)
```

`Accuracy of the model: 0.96`

```
# Getting confusion matrix
cm = confusion_matrix(expected,predicted)
print('Confusion Matrix is:',cm, sep='\n')
```

```
Confusion Matrix is:
[[50 0 0]
[ 0 47 3]
[ 0 3 47]]
```

```
#Getting classification report
cr = classification_report(expected, predicted)
print(cr)
```

```
precision-recall f1-score support
0 1.00 1.00 1.00 50
1 0.94 0.94 0.94 50
2 0.94 0.94 0.94 50
accuracy 0.96 150
macro avg 0.96 0.96 0.96 150
weighted avg 0.96 0.96 0.96 150
```

I hope after you enjoyed reading this article and finally,
you came to know about **Naive bayes algorithm****, its working and different types.
You also get an idea how you may implement it in python. **

For more such blogs/courses on data science, machine
learning, artificial intelligence and emerging new technologies do visit us at InsideAIML.

Thanks for reading…

Happy Learning…