When do you use naive Bayes in R?
If all the input features are categorical, Naive Bayes is recommended. However, in case of numeric features, it makes another strong assumption which is that the numerical variable is normally distributed. R supports a package called ‘e1071’ which provides the naive bayes training function.
How is the training of naive Bayes done?
Training Naïve Bayes can be done by evaluating an approximation algorithm in closed form in linear time, rather than by expensive iterative approximation. In simple terms, a Naïve Bayes classifier assumes that the value of a particular feature is unrelated to the presence or absence of any other feature, given the class variable.
How are naive Bayes classifiers used in machine learning?
In machine learning, Naïve Bayes classifiers are a family of simple probabilistic classifiers based on applying Bayes’ theorem with strong (naïve) independence assumptions between the features.
How are Gaussian distributions generated in naive Bayes?
Since the predictor variables here are all continuous, the Naïve Bayes classifier generates three Gaussian (Normal) distributions for each predictor variable: one for each value of the class variable Species. If you type: You will see the mean (first column) and standard deviation (second column) for the 3 class-dependent Gaussian distributions:
How is the naive Bayes model used in machine learning?
Based on Bayes Theorem, the Naive Bayes model is a supervised classification algorithm and it is commonly used in machine learning problems. In this post, we’ll learn how to use the naiveBayes function of the e1071 package to classify data.
Why do I have variable tdata in my Bayes model?
This is most likely caused by a mismatch in the encoding of factors in the training data (variable tdata in your case) and the new data used in the predict function (variable pdata ), typically that you have factor levels in the test data that are not present in the training data.
What is the accuracy of naive Bayes algorithm?
This means the ability of Naive Bayes algorithm to predict “No” cases is about 91.5% but it falls down to only 49% of the “Yes” cases resulting in an overall accuracy of 77.8% Conclusion: Can we Do any Better?