CLASSIFICATION IN R - NAIVE BAYES - Diabetes

    

 CLASSIFICATION

Description of Naive Bayes

• Naive Bayes is a probabilistic technique for constructing classifiers. 

• The characteristic assumption of the naive Bayes classifier is to consider that the value of a particular feature is independent of the value of any other feature, given the class variable.

• Despite the oversimplified assumptions mentioned previously, naive Bayes classifiers have good results in complex real-world situations. 

• An advantage of naive Bayes is that it only requires a small amount of training data to estimate the parameters necessary for classification and that the classifier can be trained incrementally.

Role / Importance

• A Naive Bayes classifier is a probabilistic machine learning model that’s used for classification task. 

• The crux of the classifier is based on the Bayes theorem.

Bayes Theorem:

• Using Bayes theorem, we can find the probability of A happening, given that B has occurred. Here, B is the evidence and A is the hypothesis.

• The assumption made here is that the predictors/features are independent. 

• That is presence of one particular feature does not affect the other. Hence it is called naive.

PROBLEM

Source Code

library(caret)

library(klaR)

diabet<-read.csv('C:/Semester 6/Data Science/diabetes.csv')

head(diabet)

split=0.80

trainIndex <- createDataPartition(diabet$Outcome, p=split, list=FALSE)

data_train <- diabet[ trainIndex,]

data_test <- diabet[-trainIndex,]

diabet$Outcome<-as.factor(diabet$Outcome)

library(naivebayes)

# train a naive bayes model

model <- naive_bayes(diabet$Outcome~diabet$BMI, data=data_train)

diabet$Outcome

# make predictions

predictions <- predict(model)

predictions

# summarize results

confusionMatrix(predictions, diabet$Outcome)

Output

Conclusion

As we can see from the result, the accuracy of the Naive Bayes model is 67%. This means the model correctly classifies 67% of the instances.