Naive Bayes Classifier in Machine Learning

  • date 15th February, 2020 |
  • by Prwatech |
  • 0 Comments

 

Naive Bayes Classifier in Machine Learning

 

Naive Bayes Classifier in Machine Learning, in this Tutorial one can learn naive bayes classifier tutorial. Are you the one who is looking for the best platform which provides information about know How Naive Bayes Algorithm Works,introduction to naive bayes algorithm, naive bayes classifier example? Or the one who is looking forward to taking the advanced Data Science Certification Course with Machine Learning from India’s Leading Data Science Training institute? Then you’ve landed on the Right Path.

 

Bayes theorem finds many uses within the applied mathematics and statistics. There’s a micro chance that you just just haven’t heard about this theorem in your life. seems that this theorem has found its way into the planet of machine learning, to make one in all the highly decorated algorithms. Naive Bayes may be a probabilistic machine learning algorithm supported the Bayes Theorem, utilized in a good style of classification tasks.

 

The Below mentioned naive bayes classifier Tutorial will help to Understand the detailed information about Naive Bayes Classifier in Machine Learning, so Just follow all the tutorials of India’s Leading Best Data Science Training institute in Bangalore and Be a Pro Data Scientist or Machine Learning Engineer.

 

Introduction to Naive Bayes Algorithm

 

Naive Bayes is a classification technique which is based on Bayes’ Theorem. It holds the assumption of independence among predictors. Bayes’ Theorem is a way of calculating a probability when we know certain other probabilities.

 

The formula is:

P(A|B) = (P(A)* P(B|A)) / P(B)

 

P(A|B): indicates how often A happens, given that B occurs.

P(B|A): indicates how often B happens, given that A occurs.

P(A): Indicates how A is on its own.

P(B): Indicates how B is on its own.

 

Let us say P(Rain) means how often there is Rain, and P(Cloudy) means how often we see Cloudy weather, then:

P(Rain|cloudy) means how often there is rain when we can see cloudy weather.
P(Cloudy|Rain) means how often we can see cloudy weather when there is rain. So, the formula kind of tells us “forwards” P(Rain|Cloudy) when we know “backwards” P(Cloudy|Rain).

 

Bayes theorem is one of the most elementary probabilistic inference algorithms which are developed by Reverend Bayes. In short, Naive Bayes classifier assumes that the existence of a particular feature in a class is distinct to the presence of any other feature. For example, a ball may be considered to be a tennis ball if it is fluorescent yellow in color, round, and about 2.7 inches in diameter. All of these properties independently contribute to the probability that this ball is a tennis ball, even if these features depend on each other or upon the existence of the other features, and hence it is called as Naive’.

 

Naive Bayes classifiers are an assembly of classification algorithms based on Bayes’ Theorem. Unlike single algorithms, it is a family of algorithms where all algorithms will share a mutual principle of having pair of independent features being classified.

 

Naive Bayes Classifier Example

 

To start with, let us consider a data set.

 

Outlook Temperature humidity windy Cricket
0 Rainy Hot High False No
1 Rainy Hot High True No
2 Overcast Hot High False Yes
3 Sunny Mild High False Yes
4 Sunny Cool Normal False Yes
5 Sunny Cool Normal True No
6 Overcast Cool Normal True Yes
7 Rainy Mild High False No
8 Rainy Cool Normal False Yes
9 Sunny Mild Normal False Yes
10 Rainy Mild Normal True Yes
11 Overcast Mild High True Yes
12 Overcast Hot Normal False Yes
13 Sunny Mild High True No

 

Let’s divide the set into two parts. One part is of features, which is feature matrix and one part is nothing but the target column which is also called response vector.

 

In this example the features included in feature matrix are ‘Outlook’, ‘Temperature’, ‘Humidity’ and ‘Windy’. Response vector contains the value of target variable which is to be predicted for each row of feature matrix. In this example the column named ‘Cricket’ is a response vector.

 

The fundamental Naïve Bayes assumes, each feature makes independent as well as equal contribution to the outcome.

 

This concept can be understood with relation to our data set as follows:

 

We assume that there is no any pair with dependent features. Example, the temperature categorized as ‘Hot’ has no dependency on humidity or the view being ‘Rainy’ has no effect on the winds. So, the features are expected to be self-governing.

 

And each feature is given the same weight. In this example, knowing only temperature and humidity we can’t forecast the outcome precisely. None of the attributes is immaterial and assumed to be contributing equally to the outcome.

 

There is needed to make some assumptions in case of continuous data, regarding the distribution of values of each feature. Based on the assumptions made regarding the distribution of P (xi | y), Naive Bayes Classifiers differ from each other.

 

How Naive Bayes Algorithm Works?

 

Now, we discuss one of such classifiers here. In case of above example, let’s indicate dependent vector by ‘y’ and the set of the features as X where it contains features as:

 

Naive Bayes Classifier Example

 

Now we can apply Bayes theorem as

Suppose we consider a case.

X= sunny, mild, Normal, False

So now we have to predict Y

In case of probability that cricket will be played

 

Naive Bayes Classifier Example

 

And in case of probability of not playing cricket we can write equation as:

 

Naive Bayes Classifier Example

 

After mathematical calculation (with reference of probability concept we can calculate probability of each feature with respect to dependent variable conditions (for y) ‘Yes’ and ‘No’.

 

Naive Bayes Classifier Example

 

So, finally for combination of:  X= sunny, mild, Normal, False

We will get the predicted value of Y As Y= Yes

 

 

In this example the data given was of discrete type. What if the data is having continuous nature? For that purpose, we have to apply Gaussian Naive Bayes. The different naive Bayes classifiers have different assumptions regarding the distribution of only by the assumptions they make regarding the distribution of P (xi | y).

 

Gaussian Naive Bayes Classifier

 

Continuous values related to each feature are assumed to be distributed according to a Gaussian distribution in Gaussian Naive Bayes. A Gaussian distribution is known as Normal distribution. It gives a bell-shaped curve, after plotting, which is symmetric about the mean of the feature values. It can be shown as follows:

 

Gaussian Naive Bayes classifier

 

The probability of the features is considered to be Gaussian. The corresponding conditional probability is given by:

 

Gaussian Conditional Probability

 

Now, we will try to implement Gaussian Naive Bayes classifier with scikit-learn.

 

# Initializing and importing libraries. Loading file

 

Import pandas as pd

Df= pd.read_csv(“Your file Path”)

 

Gaussian Conditional Probability

 

# Allotting variables for the feature matrix as(X) and response vector as (y)

 

X = df.drop([‘Outcome’],axis=’columns’)

y = df[[‘Outcome’]]

 

# Now splitting X and y into training and testing sets

 

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=0)

 

# Importing Gaussian from sklearn.naive_bayes

 

from sklearn.naive_bayes import GaussianNB

gnb = GaussianNB()

gnb.fit(X_train, y_train)

 

# Testing for test set.

 

y_pred = gnb.predict(X_test)

 

# Getting accuracy of prediction

 

Acc_NB = metrics.accuracy_score(y_test, y_pred)

from sklearn import metrics

print (“Gaussian Naive Bayes model accuracy:”, Acc_NB*100)

Gaussian Naive Bayes model accuracy: 79.16666666666666

 

We hope you understand Naive Bayes Classifier in Machine Learning.Get success in your career as a Data Scientist by being a part of the Prwatech, India’s leading Data Science training institute in Bangalore.

 

 

 

 

Leave a Reply

Your email address will not be published. Required fields are marked *

Quick Support

image image