Naive Bayes using Python- A Beginner's Tutorial

Naive Bayes using Python

In the previous article, we studied the Decision Tree. One thing that I believe is that if we can correlate anything with us or our lives, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans.

Key Terms

1. Coin

A coin has two sides, Head and Tail. If an event consists of more than one coin, then coins are considered as distinct, if not otherwise stated.

2. Die

The die has six faces marked 11, 2, 3, 4, 5 and 6. If we have more than one dice, then all dice are considered as distinct, if not otherwise stated.

3. Playing Cards

A pack of playing cards has 52 cards. There are 4 suits (spade, heart, diamond, and club) each having 13 cards. There are two colors, red (heart and diamond) and black (spade and club) each having 26 cards. In 13 cards of each suit, there are 3 face cards namely king, queen and jack so there are in all ’12 face cards. Also, there are 16 honor cards, 4 of each suit namely ace, king, queen, and jack.

Types of Experiments

1. Deterministic Experiment Those experiments, which when repeated under identical conditions produce the same result or outcome are known as a deterministic experiment,

2. Probabilistic/Random Experiment Those experiments, which when repeated under identical conditions, do not produce the same outcome every time but the outcome in a trial is one of the several possible outcomes called a random experiment.

Important Definitions

(i) Trial

Let a random experiment, be repeated under identical conditions, then the experiment is called a Trial.

(ii) Sample Space

The set of all possible outcomes of an experiment is called the sample space of the experiment and it is denoted by S.

(iii) Event

A subset of the sample space associated with a random experiment is called an event or case.

(iv) Sample Points

The outcomes of an experiment are called the sample point.

(v) Certain Event

An event that must occur, whatever be the outcomes, is called a certain or sure event.

(vi) Impossible Event

An event that cannot occur in a particular random experiment is called an impossible event.

(vii) Elementary Event

An event certainly only one sample point is called elementary event or indecomposable events.

(viii) Favorable Event

Let S be the sample space associated with a random experiment and let E ⊂ S. Then, the elementary events belonging to E are known as the favorable event to E.

(ix) Compound Events

An event certainly more than one sample point is called compound events or decomposable events.

Probability

If there are n elementary events associated with a random experiment and m of them are favorable to an event A, then the probability of happening or occurrence of A, denoted by P(A), is given by P(A) = m / n = Number of favourable cases / Total number of possible cases

Types of Events

(i) Equally Likely Events

The given events are said to be equally likely if none of them is expected to occur in preference to the other.

(ii) Mutually Exclusive Events

A set of events is said to be mutually exclusive if the happening of one excludes the happening of the other. If A and B are mutually exclusive, then P(A ∩ B) = 0

(iii) Exhaustive Events

A set of events is said to be exhaustive if the performance of the experiment always results in the occurrence of at least one of them. If E1, E2, … , En are exhaustive events, then El ∪ E2 ∪ … ∪ En = S i.e., P(E1 ∪ E2 ∪ E3 ∪ … ∪ En) = 1

(iv) Independent Events

Two events A and B associated with a random experiment are independent if the probability of occurrence or non-occurrence of A is not affected by the occurrence or non-occurrence of B. i.e., P(A ∩ B) = P(A) P(B)

The complement of an Event

Let A be an event in a sample space S~the complement of A is the set of all sample points of the space other than the sample point in A and it is denoted by, A’ or A = {n : n ∈ S, n ∉ A}

(i) P(A ∪ A’) = S

(ii) P(A ∩ A’) = φ

(iii) P(A’)’ = A

Partition of a Sample Space

The events A1, A2,…., An represent a partition of the sample space S, if they are pairwise disjoint, exhaustive and have non-zero probabilities. i.e.,

(i) Ai ∩ Aj = φ; i ≠ j; i,j= 1,2, …. ,n

(ii) A1 ∪ A2 ∪ … ∪ An = S

(iii) P(Ai) > 0, ∀ i = 1,2, …. ,n

Important Results on Probability

(i) If a set of events A1, A2,…., An are mutually exclusive, then

A1 ∩ A2 ∩ A3 ∩ …∩ An = φ

P(A1 ∪ A2 ∪ A3 ∪… ∪ An) = P(A1) + (A2) + … + P(An) and A1 ∩ A2 ∩ A3 ∩ …∩ An = 0

(ii) If a set of events A1, A2,…., An are exhaustive, then P(A1 ∪ A2 ∪ … ∪ An) = 1

(iii) The probability of an impossible event is O. i.e., P(A) = 0 if A is an impossible event. ,

(iv) Probability of any event in a sample space is 1. i.e., P(A) = 1

(v) Odds in favour of A = P(A) / P(A)

(vi) Odds in Against of A = P(A) / P(A)

(vii) Addition Theorem of Probability

(a) For two events A and B

P(A ∪ B) = P(A) + P(B) – P(A ∩ B)

(b) For three events A, B and C

P(A ∪ B ∪ C) = P(A) + P(B) + P(C) -P(A ∩ B) – P(B ∩ C) – P(A ∩ C) + P(A ∩ B ∩ C)

(viii) If A and B are two events, then P(A ∩ B) ≤ P(A) ≤ P(A ∪ B) ≤ P(A) + P(B)

(ix) If A and B are two events associated with a random experiment, then

(a) P(A ∩ B) = P(B) – P(A ∩ B)

(b) P(A ∩ B) = P(A) – P(A ∩ B)

(c)P [(A ∩ B) ∪ (A ∩ B)] = P(A) + P(B) – 2P(A ∩ B)

(d) P(A ∩ B) = 1- P(A ∪ B)

(e) P(A ∪ B) = 1- P(A ∩ B)

(f) P(A) = P(A ∩ B) + P(A ∩ B).

(g) P(B) = P(A ∩ B) + P(B ∩ A)

(x)

(a) P (exactly one of A, B occurs) = P(A) + P(B) – 2P(A ∩ B) = P(A ∪ B) – P(A ∩ B)

(b) P(neither A nor B) = P(A’ ∩ B’) = 1 – P(A ∪ B)

(xi) If A, B and C are three events, then

(a) P(exactly one of A, B, C occurs) = P(A) + P(B) + P(C) – 2P(A ∩ B) – 2P(B ∩ C) – 2P(A ∩ C) + 3P(A ∩ B ∩ C)

(b) P (at least two of A, B, C occurs) = P(A ∩ B) + P(B ∩ C) + P(C ∩ A) – 2P(A ∩ B ∩ C)

(xii)

(a) P(A ∪ B) = P(A) + P(B), if A and B are mutually exclusive events.

(b) P(A ∪ B ∪ C) = P(A) + P(B) + P(C), if A, Band C are mutually exclusive events.

(xiii) P(A) = 1- P(A)

(xiv) P(A ∪ B) = P(S) = 1, P(φ) = 0

(xv) P(A ∩ B) = P(A) x P(B), if A and B are independent events.

(xvi) If A1, A2,…., An are independent events associated with a random experiment, the probability of occurrence of at least one

= P(A1 ∪ A2 ∪…. ∪ An)

= 1 – P(A1 ∪ A2 ∪…. ∪ An)

= 1 – P(A1)P(A2)…P(An)

(xvii) If B ⊆ A, then P(A ∩ B) = P(A) – P(B)

Conditional Probability

Let A and B be two events associated with a random experiment, then, the probability of occurrence of event A under the condition that B has already occurred and P(B) ≠ 0, is called the conditional probability.

i.e., P(A/B) = P(A ∩ B) / P(B)

If A has already occurred and P (A) ≠ 0, then

P(B/A) = P(A ∩ B) / P(A)

Also, P(A / B) + P (A / B) = 1

Multiplication Theorem on Probability

(i) If A and B are two events associated with a random experiment, then

P(A ∩ B) = P(A)P(B /A), IF P(A) ≠ 0

P(A ∩ B) = P(B)P(A /B), IF P(B) ≠ 0

(ii) If A1, A2,…., An are n events associated with a random experiment, then

P(A1 ∩ A2 ∩…. ∩ An) = P(A1) P(A2 / A1) P(A3 / (A1 ∩ A2)) …P(An / (A1 ∩ A2 ∩ A3 ∩…∩A n – 1))

Total Probability

Let S be the sample space and let E1, E2,…., En be n mutually exclusive and exhaustive events associated with a random experiment. If A is any event which occurs with E1 or E2 or … or En then

P(A) = P(E1)P(A / E1) + P(E2)P(A / E2) + … + P(En) P(A / En)

Baye’s Theorem

Let S be the sample space and let E1, E2,…, En, be n mutually exclusive and exhaustive events associated With a random experiment. If A is any event which occurs with E1 or E2 or … or En then the probability of occurrence of Ei, when A occurred,

where,

P (Ei), i = 1,2, n are known as the prior probabilities
P (A / Ei), i = 1,2, , n are called the likelihood probabilities
P (Ei / A), i = 1, 2, … ,n is called the posterior probabilities

$\displaystyle Prosterior={\frac {Likelihood \mbox{*} Prior}{Evidence}}$

$P(A\mid B)={\frac {P(B\mid A)P(A)}{P(B)}}\cdot$

where A and B are events and P ( B ) ≠ 0.

P ( A ∣ B ) is a conditional probability: the likelihood of event A occurring given that B is true.
P ( B ∣ A ) is also a conditional probability: the likelihood of event B occurring given that A is true.
P ( A ) and P ( B ) are the probabilities of observing A and B independently of each other; this is known as the marginal probability.

Random Variable

Let U or S be a sample space associated with a given random experiment. A real-valued function X defined on U or S, i:e.,

X: U → R is called a random variable.

There are two types of random variables.

(i) Discrete Random Variable

If the range of the real function X: U → R is a finite set or an infinite set of real numbers, it is called a discrete random variable.

(ii) Continuous Random Variable

If the range of X is an interval (a, b) of R, then X is called a continuous random variable. e.g., In tossing of two coins S = {HH, HT, TH, TT}, let X denotes the number of heads in the tossing of two coins, then X(HH) = 2, X(TH) = 1, X(TT) = 0

Probability Distribution

If a random variable X takes values X1, X2,…., Xn with respective probabilities P1, P2,…., Pn then is known as the probability distribution of X, or Probability distribution gives the values of the random variable along with the corresponding probabilities.

Mathematical Expectation/Mean

If X is a discrete random variable which assume values X1, X2,…., Xn with respective probabilities P1, P2,…., Pn then the mean x of X is defined as

E(X) = X = P1X1 + P2X2 + … + PnXn = Σni = 1 PiXi

Important Results

(i) Variance V(X) = σ2x = E(X2) – (E(X))2

where, E(X2) = Σni = 1 x2iP(xi)

(ii) Standard Deviation √V(X) = σx = √E(X2) – (E(X))2

(iii) If Y = a X + b, then

(a) E(Y) = E(aX + b) = aE(X) + b

(b) σ2y = a2V(Y) = a2σ2x

(iv) If Z = aX2 + bX + c, then

E(Z) = E(aX2 + bX + c) = aE(X2) + bE(X) + c

Baye's Theorem Explanation using an example

Let us try to understand the above formula through an example:

Question

Talking about C-SharpCorner, if a person visits C-SharpCorner, the chances of he/she revisiting are 60%, the chances of a person liking a particular article are 75%. Chances of a person liking the article and coming back are 75%. So we need to find the probability of a person re-visiting the website given that he/she doesn't like the article.

Solution

A: A person re-visits the website

B: A person likes an article

So, P(A) = 0.6, P(A') = 0.4

P(B) = 0.75 , P(B') =0.25

P(A|B) = 0.75, P(A'|B) = 0.25

P(B|A') = P((A'|B)*P(B))/ P(A')

= (0.25*0.75)/0.4

= 0.46875

So, from the above calculations, it is clear that a person will revisit ~47% time if he/she doesn't like a particular article

When is Naive Bayes Classifier Used?

1. Real-time prediction

Naive Bayes Algorithm is fast and always ready to learn hence best suited for real-time predictions.

2. Multi-class prediction

The probability of multi-classes of any target variable can be predicted using a Naive Bayes algorithm.

3. Recommendation system

Naive Bayes classifier with the help of Collaborative Filtering builds a Recommendation System. This system uses data mining and machine learning techniques to filter the information which is not seen before and then predict whether a user would appreciate a given resource or not.

4. Text classification/ Sentiment Analysis/ Spam Filtering

Due to its better performance with multi-class problems and its independence rule, Naive Bayes algorithm performs better or have a higher success rate in text classification, Therefore, it is used in Sentiment Analysis and Spam filtering.

Difference between Bayes and Naive Bayes Algorithm

The naive Bayes classifier is an approximation to the Bayes classifier, in which we assume that the features are conditionally independent given the class instead of modeling their full conditional distribution given the class. A Bayes classifier is best interpreted as a decision rule. Suppose we seek to estimate the class of ("classify") an observation is given a vector of features. Denote the class C and the vector of features (F1, F2,…, Fk). Given a probability model underlying the data (that is, given the joint distribution of (C, F1, F2,…, Fk), the Bayes classification function chooses a class by maximizing the probability of the class given the observed features: argmaxc P(C=c∣F1=f1,…,Fk=fk)

Advantages/Features of Naive Bayes Algorithm

Easy to implement.
Fast
If the independence assumption holds then it works more efficiently than other algorithms.
It requires less training data.
It is highly scalable.
It can make probabilistic predictions.
Can handle both continuous and discrete data.
Insensitive to irrelevant features.
It can work easily with missing values.
Easy to update on the arrival of new data.
Best suited for text classification problems.

Disadvantages/Shortcomings of Naive Bayes Algorithm

The strong assumption about the features to be independent which is hardly true in real-life applications.
Data scarcity.
Chances of loss of accuracy.
Zero Frequency i.e. if the category of any categorical variable is not seen in the training data set then the model assigns a zero probability to that category and then a prediction cannot be made.

Assumptions of Naive Bayes Algorithm

All the features are independent, that is there are no dependencies between any of the features.
Each of the features is given equal or the same importance or equal weight

Things to keep in mind to get the best out of Naive Bayes Algorithm

Categorical Inputs
Naive Bayes assumes label attributes such as binary, categorical or nominal.
Gaussian Inputs
If the input variables are real-valued, a Gaussian distribution is assumed. In which case the algorithm will perform better if the univariate distributions of your data are Gaussian or near-Gaussian. This may require removing outliers (e.g. values that are more than 3 or 4 standard deviations from the mean).
Classification Problems
Naive Bayes works best with binary and multiclass classification.
Log Probabilities
The calculation of the likelihood of different class values involves multiplying a lot of small numbers together. We should use a log transform of the probabilities to avoid an underflow of numerical precision.
Kernel Functions
Rather than assuming a Gaussian distribution for numerical input values, more complex distributions can be used such as a variety of kernel density functions.
Update Probabilities
When new data becomes available, you can simply update the probabilities of your model. This can be helpful if the data changes frequently.

Types of Naive Bayes Algorithm

1. Multinomial Naive Bayes

This is mostly used for the document classification problems, i.e whether a the document belongs to the category of sports, politics, technology, etc. The features/predictors used by the classifier are the frequency of the words present in the document.

2. Bernoulli Naive Bayes

This is similar to the multinomial naive Bayes but the predictors are boolean variables. The parameters that we use to predict the class the variable takes up only values yes or no, for example, if a word occurs in the text or not.

3. Gaussian Naive Bayes

When the predictors take up a continuous value and are not discrete, we assume that these values are sampled from a gaussian distribution.

4. Semi-supervised parameter estimation

Given away to train a naive Bayes classifier from labeled data, it's possible to construct a semi-supervised training algorithm that can learn from a combination of labeled and unlabeled data by running the supervised learning algorithm in a loop

What is Naive Bayes?

Type	Long	Not Long	Sweet	Not Sweet	Yellow	Not Yellow	Total
Banana	400	100	350	150	450	50	500
Orange	0	300	150	150	300	0	300
Other	100	100	150	50	50	150	200
Total	500	500	650	350	800	200	1000

So the objective of the classifier is to predict if a given fruit is a ‘Banana’ or ‘Orange’ or ‘Other’ when only the 3 features (long, sweet and yellow) are known.

So to predict this we need to find 3 probabilities. Let's start

1. We first calculate the "Prior" probabilities for each of the class of fruits

P[Y=Banana] = 500/1000 = 0.5

P[Y=Orange] = 300/1000 = 0.3

P[Y=Other] = 200/1000 = 0.2

2. We then compute the probability of evidence that goes in the denominator

P[x1=Long] = 500/100 = 0.5

P[x2=Sweet] = 650/100 = 0.65

P[x3=Yellow] = 800/100 = 0.8

3. Now we calculate the probability of likelihood of evidences that goes in the numerator

P[x1=Long | Y=Banana] = 400/500 = 0.8

P[x2=Sweet | Y=Banana] = 350/500 = 0.7

P[x3=Yellow | Y=Banana] = 450/500 = 0.9

4. At the end we substitute all the values in the Naive Bayes Formula,

a. P(Banana | Long, Sweet and Yellow)

= ((P(Long | Banana) * P(Sweet | Banana) * P(Yellow | Banana))*P(Banana))/ (P(Long) * P(Sweet) * P(Yellow))

= ((0.8 * 0.7 * 0.9) * 0.5)/(0.5 * 0.65 * 0.8)

= 0.97

b. P(Orange | Long, Sweet and Yellow)

= ((P(Long | Orange) * P(Sweet | Orange) * P(Yellow | Orange))*P(Orange))/ (P(Long) * P(Sweet) * P(Yellow))

= 0

c. P(Others | Long, Sweet and Yellow)

= ((P(Long | Others) * P(Sweet | Others) * P(Yellow | Others))*P(Others))/ (P(Long) * P(Sweet) * P(Yellow))

= 0.07

So, from the Naive Bayes Classifier, we predict the fruit is a Banana.

Python Implementation of Decision Tree

Let's take the example of the IRIS dataset, you can directly import it from the sklearn dataset repository or download it from the article. Feel free to use any dataset, there some very good datasets available on kaggle and with Google Colab.

Before we start with this, it is highly recommended you read the following tutorials

1. Using functions

from csv import reader
from math import sqrt
from math import exp
from math import pi
# Load a CSV file
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
# Convert string column to float
def str_column_to_float(dataset, column):
for row in dataset:
row[column] = float(row[column].strip())
# Convert string column to integer
def str_column_to_int(dataset, column):
class_values = [row[column] for row in dataset]
unique = set(class_values)
lookup = dict()
for i, value in enumerate(unique):
lookup[value] = i
print('[%s] => %d' % (value, i))
for row in dataset:
row[column] = lookup[row[column]]
return lookup
# Split the dataset by class values, returns a dictionary
def separate_by_class(dataset):
separated = dict()
for i in range(len(dataset)):
vector = dataset[i]
class_value = vector[-1]
if (class_value not in separated):
separated[class_value] = list()
separated[class_value].append(vector)
return separated
# Calculate the mean of a list of numbers
def mean(numbers):
return sum(numbers)/float(len(numbers))
# Calculate the standard deviation of a list of numbers
def stdev(numbers):
avg = mean(numbers)
variance = sum([(x-avg)**2 for x in numbers]) / float(len(numbers)-1)
return sqrt(variance)
# Calculate the mean, stdev and count for each column in a dataset
def summarize_dataset(dataset):
summaries = [(mean(column), stdev(column), len(column)) for column in zip(*dataset)]
del(summaries[-1])
return summaries
# Split dataset by class then calculate statistics for each row
def summarize_by_class(dataset):
separated = separate_by_class(dataset)
summaries = dict()
for class_value, rows in separated.items():
summaries[class_value] = summarize_dataset(rows)
return summaries
# Calculate the Gaussian probability distribution function for x
def calculate_probability(x, mean, stdev):
exponent = exp(-((x-mean)**2 / (2 * stdev**2 )))
return (1 / (sqrt(2 * pi) * stdev)) * exponent
# Calculate the probabilities of predicting each class for a given row
def calculate_class_probabilities(summaries, row):
total_rows = sum([summaries[label][0][2] for label in summaries])
probabilities = dict()
for class_value, class_summaries in summaries.items():
probabilities[class_value] = summaries[class_value][0][2]/float(total_rows)
for i in range(len(class_summaries)):
mean, stdev, _ = class_summaries[i]
probabilities[class_value] *= calculate_probability(row[i], mean, stdev)
return probabilities
# Predict the class for a given row
def predict(summaries, row):
probabilities = calculate_class_probabilities(summaries, row)
best_label, best_prob = None, -1
for class_value, probability in probabilities.items():
if best_label is None or probability > best_prob:
best_prob = probability
best_label = class_value
return best_label
# Make a prediction with Naive Bayes on Iris Dataset
filename = 'iris.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])-1):
str_column_to_float(dataset, i)
# convert class column to integers
str_column_to_int(dataset, len(dataset[0])-1)
# fit model
model = summarize_by_class(dataset)
# define a new record
row = [5.7,2.9,4.2,1.3]
# predict the label
label = predict(model, row)
print('Data=%s, Predicted: %s' % (row, label))

The output that I am getting is

[Iris-versicolor] => 0

[Iris-setosa] => 1

[Iris-virginica] => 2

Data=[5.7, 2.9, 4.2, 1.3], Predicted: 0

2. Using Sklearn

from sklearn import datasets
#Load dataset
wine = datasets.load_wine()
# print the names of the 13 features
print ("Features: ", wine.feature_names)
# print the label type of wine(class_0, class_1, class_2)
print ("Labels: ", wine.target_names)
# Import train_test_split function
from sklearn.model_selection import train_test_split
# Split dataset into training set and test set
X_train, X_test, y_train, y_test = train_test_split(wine.data, wine.target, test_size=0.3,random_state=109)
#Import Gaussian Naive Bayes model
from sklearn.naive_bayes import GaussianNB
#Create a Gaussian Classifier
gnb = GaussianNB()
#Train the model using the training sets
gnb.fit(X_train, y_train)
#Predict the response for test dataset
y_pred = gnb.predict(X_test)
print("y_pred: ",y_pred)
#Import scikit-learn metrics module for accuracy calculation
from sklearn import metrics
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

The output that I got is

Features: ['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']

Labels: ['class_0' 'class_1' 'class_2']

y_pred: [0 0 1 2 0 1 0 0 1 0 2 2 2 2 0 1 1 0 0 1 2 1 0 2 0 0 1 2 0 1 2 1 1 0 1 1 0 2 2 0 2 1 0 0 0 2 2 0 1 1 2 0 0 2]

Accuracy: 0.9074074074074074

3. Using TensorFlow

from IPython import embed
from matplotlib import colors
from matplotlib import pyplot as plt
from sklearn import datasets
import numpy as np
import tensorflow as tf
from sklearn.utils.fixes import logsumexp
import numpy as np
class TFNaiveBayesClassifier:
dist = None
def fit(self, X, y):
# Separate training points by class (nb_classes * nb_samples * nb_features)
unique_y = np.unique(y)
points_by_class = np.array([
[x for x, t in zip(X, y) if t == c]
for c in unique_y])
# Estimate mean and variance for each class / feature
# shape: nb_classes * nb_features
mean, var = tf.nn.moments(tf.constant(points_by_class), axes=[1])
# Create a 3x2 univariate normal distribution with the
# known mean and variance
self.dist = tf.distributions.Normal(loc=mean, scale=tf.sqrt(var))
def predict(self, X):
assert self.dist is not None
nb_classes, nb_features = map(int, self.dist.scale.shape)
# Conditional probabilities log P(x|c) with shape
# (nb_samples, nb_classes)
cond_probs = tf.reduce_sum(
self.dist.log_prob(
tf.reshape(
tf.tile(X, [1, nb_classes]), [-1, nb_classes, nb_features])),
axis=2)
# uniform priors
priors = np.log(np.array([1. / nb_classes] * nb_classes))
# posterior log probability, log P(c) + log P(x|c)
joint_likelihood = tf.add(priors, cond_probs)
# normalize to get (log)-probabilities
norm_factor = tf.reduce_logsumexp(
joint_likelihood, axis=1, keep_dims=True)
log_prob = joint_likelihood - norm_factor
# exp to get the actual probabilities
return tf.exp(log_prob)
if __name__ == '__main__':
iris = datasets.load_iris()
# Only take the first two features
X = iris.data[:, :2]
y = iris.target
tf_nb = TFNaiveBayesClassifier()
tf_nb.fit(X, y)
# Create a regular grid and classify each point
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
xx, yy = np.meshgrid(np.linspace(x_min, x_max, 30),
np.linspace(y_min, y_max, 30))
s = tf.Session()
Z = s.run(tf_nb.predict(np.c_[xx.ravel(), yy.ravel()]))
# Extract probabilities of class 2 and 3
Z1 = Z[:, 1].reshape(xx.shape)
Z2 = Z[:, 2].reshape(xx.shape)
# Plot
fig = plt.figure(figsize=(5, 3.75))
ax = fig.add_subplot(111)
ax.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Set1,
edgecolor='k')
# Swap signs to make the contour dashed (MPL default)
ax.contour(xx, yy, -Z1, [-0.5], colors='k')
ax.contour(xx, yy, -Z2, [-0.5], colors='k')
ax.set_xlabel('Sepal length')
ax.set_ylabel('Sepal width')
ax.set_title('TensorFlow decision boundary')
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xticks(())
ax.set_yticks(())
plt.tight_layout()
fig.savefig('tf_iris.png', bbox_inches='tight')

The output that I got is:

Conclusion

In this article, we studied key terms, types of experiments, important definitions, probability, types of events, conditional probability, total probability, Baye's theorem, random variable, probability distributions, Baye's theorem explanation using an example, when is naive Bayes classifier used, difference between Bayes and naive Bayes algorithms, advantages of naive Bayes algorithm, disadvantages of naive Bayes algorithm, assumptions of naive Bayes algorithm, things to keep in mind to get the best out of naive Bayes, types of naive Bayes algorithm, what is naive Bayes, Python implementation of naive Bayes algorithm using functions, sklearn, and TensorFlow. Hope you were able to understand each and everything. For any doubts, please comment on your query.

In the next article, we will learn about KNN.

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