Why is Naive Bayes a good classifier?
Advantages. It is easy and fast to predict the class of the test data set. It also performs well in multi-class prediction. When assumption of independence holds, a Naive Bayes classifier performs better compare to other models like logistic regression and you need less training data.
What are the advantages and disadvantages of using Naive Bayes?
Naïve Bayes Algorithm's Advantages and DisadvantagesIt is simple and easy to implement.It doesn't require as much training data.It handles both continuous and discrete data.It is highly scalable with the number of predictors and data points.It is fast and can be used to make real-time predictions.More items...
What is the benefit of Naive Bayes Brainly?
Answer: The Naive Bayes algorithm affords fast, highly scalable model building and scoring. It scales linearly with the number of predictors and rows. ... Naive Bayes can be used for both binary and multiclass classification problems.
What is advantage of Naive Bayes over decision tree?
Decision tree vs naive Bayes : Decision tree is a discriminative model, whereas Naive bayes is a generative model. Decision trees are more flexible and easy. Decision tree pruning may neglect some key values in training data, which can lead the accuracy for a toss.
What are the limitations of Naive Bayes?
Disadvantages of Naive BayesIf your test data set has a categorical variable of a category that wasn't present in the training data set, the Naive Bayes model will assign it zero probability and won't be able to make any predictions in this regard. ... This algorithm is also notorious as a lousy estimator.More items...•