How would you choose between naive Bayes and 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.
Is naive Bayes used for classification or regression?
Naive Bayes classifier (Russell, & Norvig, 1995) is another feature-based supervised learning algorithm. It was originally intended to be used for classification tasks, but with some modifications it can be used for regression as well (Frank, Trigg, Holmes, & Witten, 2000) .
Which is better decision tree or naive Bayes classifier?
Build a decision tree and build a naive bayes classifier then have a shoot out using the training and validation data you have. Which ever performs best will more likely perform better in the field.
Which is the most efficient naive Bayes algorithm?
Two algorithms are: § weka.classifiers.j48.J48: C4.5 decision trees. § weka.classifiers.NaiveBayes: Naïve Bayes. C4.5 is the most popular and the most efficient algorithm in decision tree-based approach. A decision tree algorithm creates a tree model by using values of only one attribute at a time.
How does the Bayes decision tree algorithm work?
Flowchart of Naïve Bayes decision tree algorithm. The classification tree literally creates a tree with branches, nodes, and leaves that lets us take an unknown data point and move down the tree, applying the attributes of the data point to the tree until a leaf is reached and the unknown output of the data point can be determined.
When to use classification in a decision tree?
It specifies the class to which data elements belong to and is best used when the output has finite and discrete values. It predicts a class for an input variable as well. There are two types of Classification: Binomial: This is a type of classification where algorithms divide the data into two classes.