Get Started With Naive Bayes Algorithm: Theory & Implementation

Surabhi 14 Jul, 2023
7 min read

Introduction

Naive Bayes is a machine learning algorithm that is used by data scientists for classification. The naive Bayes algorithm works based on the Bayes theorem. Before explaining Naive Bayes, first, we should discuss Bayes Theorem. Bayes theorem is used to find the probability of a hypothesis with given evidence. This beginner-level article intends to introduce you to the Naive Bayes algorithm and explain its underlying concept and implementation.

conditional probability | naive bayes algorithm

In this equation, using Bayes theorem, we can find the probability of A, given that B occurred. A is the hypothesis, and B is the evidence.

P(B|A) is the probability of B given that A is True.

P(A) and P(B) are the independent probabilities of A and B.

Learning Objectives

  • Learn the concept behind the Naive Bayes algorithm.
  • See the steps involved in the naive Bayes algorithm
  • Practice the step-by-step implementation of the algorithm.

This article was published as a part of the Data Science Blogathon.

What Is the Naive Bayes Classifier Algorithm?

The Naive Bayes classifier algorithm is a machine learning technique used for classification tasks. It is based on Bayes’ theorem and assumes that features are conditionally independent of each other given the class label. The algorithm calculates the probability of a data point belonging to each class and assigns it to the class with the highest probability.

Naive Bayes is known for its simplicity, efficiency, and effectiveness in handling high-dimensional data. It is commonly used in various applications, including text classification, spam detection, and sentiment analysis.

Naive Bayes Theorem: The Concept Behind the Algorithm

Let’s understand the concept of the Naive Bayes Theorem and how it works through an example. We are taking a case study in which we have the dataset of employees in a company, our aim is to create a model to find whether a person is going to the office by driving or walking using the salary and age of the person.

Naive Bayes graph

In the above image, we can see 30 data points in which red points belong to those who are walking and green belong to those who are driving. Now let’s add a new data point to it. Our aim is to find the category that the new point belongs to

Naive Bayes new data

Note that we are taking age on the X-axis and Salary on the Y-axis. We are using the Naive Bayes algorithm to find the category of the new data point. For this, we have to find the posterior probability of walking and driving for this data point. After comparing, the point belongs to the category having a higher probability.

In the above image, we can see 30 data points in which red points belong to those who are walking and green belong to those who are driving. Now let’s add a new data point to it. Our aim is to find the category that the new point belongs to

The posterior probability of walking for the new data point is:

posterior probability | naive bayes algorithm

and that for the driving is:

Naive Bayes posterior probability

Steps Involved in the Naive Bayes Classifier Algorithm

Step 1: We have to find all the probabilities required for the Bayes theorem for the calculation of posterior probability.

P(Walks) is simply the probability of those who walk among all.

Naive Bayes Algorithm step 1

In order to find the marginal likelihood, P(X), we have to consider a circle around the new data point of any radii, including some red and green points.

Naive Bayes Algorithm Step 1 graph
probability | naive bayes algorithm

P(X|Walks) can be found by: 

P(X|Walks) | naive bayes algorithm

Now we can find the posterior probability using the Bayes theorem,

Bayes theorem  | naive bayes algorithm

Step 2: Similarly, we can find the posterior probability of Driving, and it is 0.25

Step 3: Compare both posterior probabilities. When comparing the posterior probability, we can find that P(walks|X) has greater values, and the new point belongs to the walking category.

source: Unsplash

Source: Unsplash

Implementation of Naive Bayes in Python Programming

Now let’s implement Naive Bayes step by step using the python programming language

We are using the Social network ad dataset. The dataset contains the details of users on a social networking site to find whether a user buys a product by clicking the ad on the site based on their salary, age, and gender.

Naive Bayes data

Step 1: Importing the libraries

Let’s start the programming by importing the essential libraries required.

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sklearn

Step 2: Importing the dataset
Python Code:

Since our dataset contains character variables, we have to encode it using LabelEncoder.

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
X[:,0] = le.fit_transform(X[:,0])

Step 3: Train test splitting

We are splitting our data into train and test datasets using the scikit-learn library. We are providing the test size as 0.20, which means our training data contains 320 training sets, and the test sample contains 80 test sets.

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

Step 4: Feature scaling

Next, we are doing feature scaling to the training and test set of independent variables.

from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

Step 5: Training the Naive Bayes model on the training set

from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

Let’s predict the test results

y_pred  =  classifier.predict(X_test)

Predicted and actual value

 y_pred  
train data
y_test
test data

For the first 8 values, both are the same. We can evaluate our matrix using the confusion matrix and accuracy score by comparing the predicted and actual test values

from sklearn.metrics import confusion_matrix,accuracy_score
cm = confusion_matrix(y_test, y_pred)
ac = accuracy_score(y_test,y_pred)

confusion matrix

ac – 0.9125

confusion matrix

Accuracy is good. Note that you can achieve better results for this problem using different algorithms.

Full Python Tutorial

# Importing the libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd

# Importing the dataset
dataset = pd.read_csv('Social_Network_Ads.csv')
X = dataset.iloc[:, [2, 3]].values
y = dataset.iloc[:, -1].values

# Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# Training the Naive Bayes model on the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB()
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix, accuracy_score
ac = accuracy_score(y_test,y_pred)
cm = confusion_matrix(y_test, y_pred)

What Are the Assumptions Made by the Naive Bayes Algorithm?

There are several variants of Naive Bayes, such as Gaussian Naive Bayes, Multinomial Naive Bayes, and Bernoulli Naive Bayes. Each variant has its own assumptions and is suited for different types of data. Here are some assumptions that the Naive Bayers algorithm makes:

  1. The main assumption is that it assumes that the features are conditionally independent of each other.
  2. Each of the features is equal in terms of weightage and importance.
  3. The algorithm assumes that the features follow a normal distribution.
  4. The algorithm also assumes that there is no or almost no correlation among features.

Conclusion

The naive Bayes algorithm is a powerful and widely-used machine learning algorithm that is particularly useful for classification tasks. This article explains the basic math behind the Naive Bayes algorithm and how it works for binary classification problems. Its simplicity and efficiency make it a popular choice for many data science applications. we have covered most concepts of the algorithm and how to implement it in Python. Hope you liked the article, and do not forget to practice algorithms.

Key Takeaways

  • Naive Bayes is a probabilistic classification algorithm(binary o multi-class) that is based on Bayes’ theorem.
  • There are different variants of Naive Bayes, which can be used for different tasks and can even be used for regression problems.
  • Naive Bayes can be used for a variety of applications, such as spam filtering, sentiment analysis, and recommendation systems.

Frequently Asked Questions

Q1. When should we use a naive Bayes classifier?

A. The naive Bayes classifier is a good choice when you want to solve a binary or multi-class classification problem when the dataset is relatively small and the features are conditionally independent. It is a fast and efficient algorithm that can often perform well, even when the assumptions of conditional independence do not strictly hold. Due to its high speed, it is well-suited for real-time applications. However, it may not be the best choice when the features are highly correlated or when the data is highly imbalanced.

Q2. What is the difference between Bayes Theorem and Naive Bayes Algorithm?

A. Bayes theorem provides a way to calculate the conditional probability of an event based on prior knowledge of related conditions. The naive Bayes algorithm, on the other hand, is a machine learning algorithm that is based on Bayes’ theorem, which is used for classification problems.

Q3. Is Naive Bayes a regression technique or classification technique?

It is not a regression technique, although one of the three types of Naive Bayes, called Gaussian Naive Bayes, can be used for regression problems.

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Surabhi 14 Jul, 2023

Frequently Asked Questions

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Responses From Readers

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Onkar
Onkar 16 Jan, 2021

Awesome explanation ☺ This will clear all the doubts and its very helful for newbies. Keep up the good work 👍

Ryan
Ryan 19 Jan, 2021

Hi, Great post;) I would like to ask when estimating the marginal likelihood P(X), we need to draw a circle around the new data, how should we choose the radius in order to increase accuracy of the estimation? And how is the radius or metric used going too affect the accuracy? Is there any book you can recommend for this topic? Thank you so much.

Sania
Sania 26 Apr, 2021

Thanks Surbhi! Easy to understand.

Karim
Karim 15 Aug, 2022

Hi thanks for the explanation, and have a question why you are applying feature scaling? Thanks