Data Analytics with Data Science Algorithm

Data Analytics with Data Science Algorithm

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  Data Analytics withData Science- Naïve Bayes Classifier Algorithm By Dr. D.Shunmuga Kumari, Assistant Professor, Department of information Technology,
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  • Supervised learningis a machine learning method in which models are trained using labeled data. • Labeled data is data that has been tagged with a correct answer or classification. • Once the training is completed, we will test the model by giving the new set of animal. The model will identify the animal and predict the output using a suitable algorithm.
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  • Unsupervised learningis another machine learning method in which patterns inferred from the unlabeled input data. • The goal of unsupervised learning is to find the structure and patterns from the input data. • Unsupervised learning does not need any supervision. Instead, it finds patterns from the data by its own.
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  • Naïve Bayesalgorithm 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.
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  Why is itcalled 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.
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  The formula forBayes' 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.  P(A) is Prior Probability: Probability of hypothesis before observing the evidence.  P(B) is Marginal Probability: Probability of Evidence.
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  Working of NaïveBayes' Classifier: Working of Naïve Bayes' Classifier can be understood with the help of the below example: Suppose we have a dataset of weather conditions and corresponding target variable "Play". So using this dataset we need to decide that whether we should play or not on a particular day according to the weather conditions. So to solve this problem, we need to follow the below steps: Convert the given dataset into frequency tables. Generate Likelihood table by finding the probabilities of given features. Now, use Bayes theorem to calculate the posterior probability.
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  Problem: If theweather is sunny, then the Player should play or not? Solution: To solve this, first consider the below dataset: Outlook Play 0 Rainy Yes 1 Sunny Yes 2 Overcast Yes 3 Overcast Yes 4 Sunny No 5 Rainy Yes 6 Sunny Yes 7 Overcast Yes 8 Rainy No 9 Sunny No 10 Sunny Yes 11 Rainy No 12 Overcast Yes 13 Overcast Yes
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  I. Frequency tablefor the Weather Conditions: Weather Yes No Overcast 5 0 Rainy 2 2 Sunny 3 2 Total 10 5
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  11 II Likelihood tableweather 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 All 4/14=0.29 10/14=0.71
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  12 III.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.
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  13 Advantages of NaïveBayes Classifier: • Naïve Bayes is one of the fast and easy ML algorithms to predict a class of datasets. • It can be used for Binary as well as Multi-class Classifications. • It performs well in Multi-class predictions as compared to the other Algorithms. • It is the most popular choice for text classification problems. Disadvantages of Naïve Bayes Classifier: • Naive Bayes assumes that all features are independent or unrelated, so it cannot learn the relationship between features.
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  14 Applications of NaïveBayes Classifier: It is used for Credit Scoring. It is used in medical data classification. It can be used in real-time predictions because Naïve Bayes Classifier is an eager learner. It is used in Text classification such as Spam filtering and Sentiment analysis.