1 - Naive Bayes Classifier in Machine Learning - Javatpoint

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10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 2/17 Naïve Bayes Classifier Algorithm Naïve Bayes algorithm is a supervised learning algorithm, which is based on Bayes theorem and used for solving classification problems. It is mainly used in text classification that includes a high-dimensional training dataset. Naïve Bayes Classifier is one of the simple and most effective Classification algorithms which helps in building the fast machine learning models that can make quick predictions. It is a probabilistic classifier, which means it predicts on the basis of the probability of an object . Some popular examples of Naïve Bayes Algorithm are spam filtration, Sentimental analysis, and classifying articles . Why is it called Naïve Bayes? The Naïve Bayes algorithm is comprised of two words Naïve and Bayes, Which can be described as: Naïve : It is called Naïve because it assumes that the occurrence of a certain feature is independent of the occurrence of other features. Such as if the fruit is identified on the bases of color, shape, and taste, then red, spherical, and sweet fruit is recognized as an apple. Hence each feature individually contributes to identify that it is an apple without depending on each other. Bayes : It is called Bayes because it depends on the principle of Bayes' Theorem . Bayes' Theorem: Bayes' theorem is also known as Bayes' Rule or Bayes' law , which is used to determine the probability of a hypothesis with prior knowledge. It depends on the conditional probability. The formula for Bayes' theorem is given as: Where, P(A|B) is Posterior probability : Probability of hypothesis A on the observed event B. P(B|A) is Likelihood probability : Probability of the evidence given that the probability of a hypothesis is true.

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 4/17 7 Overcast Yes 8 Rainy No 9 Sunny No 10 Sunny Yes 11 Rainy No 12 Overcast Yes 13 Overcast Yes Frequency table for the Weather Conditions: Weather Yes No Overcast 5 0 Rainy 2 2 Sunny 3 2 Total 10 5 Likelihood table weather condition: Weather No Yes Overcast 0 5 5/14= 0.35 Rainy 2 2 4/14=0.29 Sunny 2 3 5/14=0.35

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 5/17 All 4/14=0.29 10/14=0.71 Applying Bayes'theorem: P(Yes|Sunny)= P(Sunny|Yes)*P(Yes)/P(Sunny) P(Sunny|Yes)= 3/10= 0.3 P(Sunny)= 0.35 P(Yes)=0.71 So P(Yes|Sunny) = 0.3*0.71/0.35= 0.60 P(No|Sunny)= P(Sunny|No)*P(No)/P(Sunny) P(Sunny|NO)= 2/4=0.5 P(No)= 0.29 P(Sunny)= 0.35 So P(No|Sunny)= 0.5*0.29/0.35 = 0.41 So as we can see from the above calculation that P(Yes|Sunny)>P(No|Sunny) Hence on a Sunny day, Player can play the game. Advantages of Naïve Bayes Classifier: Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets.

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 7/17 Fitting Naive Bayes to the Training set Predicting the test result Test accuracy of the result(Creation of Confusion matrix) Visualizing the test set result. 1) Data Pre-processing step: In this step, we will pre-process/prepare the data so that we can use it efficiently in our code. It is similar as we did in data-pre-processing . The code for this is given below: Importing the libraries import numpy as nm import matplotlib.pyplot as mtp import pandas as pd # Importing the dataset dataset = pd.read_csv( 'user_data.csv' ) x = dataset.iloc[:, [2, 3]].values y = dataset.iloc[:, 4].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 = 0.25, 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) In the above code, we have loaded the dataset into our program using " dataset = pd.read_csv('user_data.csv') . The loaded dataset is divided into training and test set, and then we have scaled the feature variable.

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 8/17 The output for the dataset is given as:

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 10/17 The above output shows the result for prediction vector y_pred and real vector y_test. We can see that some predications are different from the real values, which are the incorrect predictions. 4) Creating Confusion Matrix: Now we will check the accuracy of the Naive Bayes classifier using the Confusion matrix. Below is the code for it: # Making the Confusion Matrix from sklearn.metrics import confusion_matrix cm = confusion_matrix(y_test, y_pred) Output:

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 11/17 As we can see in the above confusion matrix output, there are 7+3= 10 incorrect predictions, and 65+25=90 correct predictions. 5) Visualizing the training set result: Next we will visualize the training set result using Naïve Bayes Classifier. Below is the code for it: # Visualising the Training set results from matplotlib.colors import ListedColormap x_set, y_set = x_train, y_train X1, X2 = nm.meshgrid(nm.arange(start = x_set[:, 0].min() - 1, stop = x_set[:, 0].max() + 1, step = 0.01 nm.arange(start = x_set[:, 1].min() - 1, stop = x_set[:, 1].max() + 1, step = 0.01)) mtp.contourf(X1, X2, classifier.predict(nm.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape), alpha = 0.75, cmap = ListedColormap(( 'purple' , 'green' ))) mtp.xlim(X1.min(), X1.max()) mtp.ylim(X2.min(), X2.max()) for i, j in enumerate(nm.unique(y_set)): mtp.scatter(x_set[y_set == j, 0], x_set[y_set == j, 1], c = ListedColormap(( 'purple' , 'green' ))(i), label = j) mtp.title( 'Naive Bayes (Training set)' ) mtp.xlabel( 'Age' ) mtp.ylabel( 'Estimated Salary' ) mtp.legend() mtp.show()

10/01/2023, 00:06 Naive Bayes Classifier in Machine Learning - Javatpoint https://www.javatpoint.com/machine-learning-naive-bayes-classifier 13/17 ← Prev Next → The above output is final output for test set data. As we can see the classifier has created a Gaussian curve to divide the "purchased" and "not purchased" variables. There are some wrong predictions which we have calculated in Confusion matrix. But still it is pretty good classifier. Youtube For Videos Join Our Youtube Channel: Join Now Feedback Send your Feedback to feedback@javatpoint.com

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