In a world where many m anual operations are mechanized, the definition of the word ‘manual’ is evolving. Computers can play chess, perform surgery, and develop into smarter, more humanlike machines with the aid of machine learning algorithms. Want to know more about NAIVE BAYES ALGORITHM visit here.. https://www.rangtech.com/blog/naive-bayes-algorithm

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NAIVE BAYES ALGORITHM
AI / MACHINE LEARNING
In a world where many manual operations are mechanized, the
definition of the word ‘manual’ is evolving. Computers can play
chess, perform surgery, and develop into smarter, more
humanlike machines with the aid of machine learning
algorithms.
What is Naive Bayes Algorithm?
The machine learning algorithm is divided into categories, in
which Naive Bayes Algorithm falls under the umbrella of
supervised machine learning algorithm. It is a classification
method built on the Bayes Theorem and predicated on the
idea of predictor independence. A Naive Bayes classifier
believes that the presence of one feature in a class has nothing
to do with the presence of any other feature. For example
The Bayes model is simple to construct and especially helpful
for very big data sets. Along with being straightforward, Naive
Bayes is known to perform better than even the most complex
classification techniques.
It provides a way of calculating the posterior probability by the
given equation below:
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2. Where P(c|x) = posterior probability of class (c) given
predictor (x).
P(x|c) = Prior probability of predictor.
Types of Naive Bayes Algorithm
Three different Naive Bayes model types may be found in the
Scikit-Learn library which are as follows: -
Multinominal Naive Bayes
Feature vectors represent the frequencies with which certain
events have been generated by a multinomial distribution. This
is the event model that is typically used for document
classification.
Bernoulli naive bayes:
In the multivariate Bernoulli event model, features are
independent Booleans (binary variables) describing inputs. Like
the multinomial model, this model is popular used for
document classification tasks, where binary term occurrence
(i.e., a word occurs in a document or not) features are used
rather than term frequencies (i.e., frequency of a word in the
document).
Gaussian Naive Bayes:
We assume that the values of the predictors are samples from
a gaussian distribution when they take up a continuous value
and are not discrete.
Below code can be implemented of Gaussian Naive Bayes
classifier using scikit-learn.
# load the iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
# store the feature matrix (X) and response vector (y)
X = iris.data
y = iris.target
# 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=0.4,
random_state=1)
# training the model on training set
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
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3. # making predictions on the testing set
y_pred = gnb.predict(X_test)
# comparing actual response values (y_test) with predicted response
values (y_pred)
from sklearn import metrics
print("Gaussian Naive Bayes model accuracy(in %):",
metrics.accuracy_score(y_test, y_pred)*100)
Output:
Gaussian Naive Bayes model accuracy (in %): 95.0
Applications of Naive Bayes Algorithm/Classifier
Real-time Prediction: Naive Bayes is a quick classifier that is
eager to learn. As a result, it might be applied to real-time
prediction.
Multi-class Prediction: This algorithm is very widely renowned
for its ability to predict many classes. Here, we can forecast
the likelihood of several target variable classes.
Sentiment analysis, spam filtering, and text classification:
Because they perform better in multi-class situations and
follow the independence criterion, naive Bayes classifiers are
frequently employed in text classification and have a greater
success rate than other methods. It is therefore frequently used
in Sentiment Analysis and Spam Filtering (to identify spam e-
mail) (in social media analysis, to identify positive and negative
customer sentiments)
Recommendation system: Naive Bayes Classifier and
Collaborative Filtering work together to create a system that
filters opportunistic information and forecasts whether a user
will find a specific resource appealing or not.
Advantages of Naive Bayes Algorithm
A Naive Bayes classifier outperforms other models when
the independent predictor assumption is valid.
To estimate the test data, Naive Bayes just needs a
modest amount of training data. So, there is a shorter
training period.
It's simple to use Naive Bayes.
Disadvantages of Naive Bayes Algorithm
The main premise of Naive Bayes is that independent
predictors exist. All of the attributes are implicitly
assumed to be independent of one another by Naive
Bayes. We rarely find a set of predictors that are entirely
independent in the real world.
If a categorical variable in the test data set has a
category that wasn't included in the training data set, the
model will give it a probability of 0 (zero), and it won't be
able to predict anything. This is commonly referred to as
Zero Frequency. We can utilize the smoothing method to
resolve this. Laplace estimate is one of the simplest
smoothing methods.
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Headquartered in New Jersey,
Rang Technologies has
dedicated over a decade
delivering innovative solutions
and best talent to help
businesses get the most out of
the latest technologies in their
digital transformation journey.
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