What is the Naive Bayes algorithm?

It is a classification technique based on Bayes’ Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.

The Naive Bayes model is easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods.

Bayes theorem provides a way of calculating posterior probability P(c|x) from P(c), P(x), and P(x|c). Look at the equation below:

Step 1: Convert the data set into a frequency table

Step 2: Create a Likelihood table by finding the probabilities like Overcast probability = 0.29 and probability of playing is 0.64.

Step 3: Now, use Naive Bayesian equation to calculate the posterior probability for each class. The class with the highest posterior probability is the outcome of prediction.

Naive Bayes using sklearn:

Code:

import pandas as pd

data=pd.read_csv('IRIS.csv')

data

	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa
...	...	...	...	...	...
145	6.7	3.0	5.2	2.3	Iris-virginica
146	6.3	2.5	5.0	1.9	Iris-virginica
147	6.5	3.0	5.2	2.0	Iris-virginica
148	6.2	3.4	5.4	2.3	Iris-virginica
149	5.9	3.0	5.1	1.8	Iris-virginica

150 rows × 5 columns

x = data.iloc[:,0:4].values  

y = data.iloc[:,4].values

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 = 1)

from sklearn.naive_bayes import GaussianNB  

classifier = GaussianNB()  

classifier.fit(x_train, y_train)  

>>GaussianNB(priors=None, var_smoothing=1e-09)

y_pred = classifier.predict(x_test)  

y_pred

my_test=[[5.1, 3.5, 1.4, 0.2]]

print(classifier.predict(my_test))

>>['Iris-setosa']