Module 11: Naive Bayesian Classifiers and Laplace Estimator
A Naive Bayesian Classifier is an extremely powerful general issue classifier that performs well for most classification problems. In addition to providing a predicted classification, it also provides a probability of that classification making it both intuitive and accurate for risk based approaches.
The dataset to be used in this module is the CreditRisk dataset used in module 7, however some consideration needs to be given to the fact that this is contains come continuous data which is not, by default, appropriate for Bayesian analysis, as Bayesian analysis is a question of probability.
While it is clearly simpler, for the purposes of these procedures, to provide a clean dataset it allows for the introduction of some more advanced data frame manipulation techniques and cements that notion that continuous data is not appropriate for this modelling tool.
Table of contents
- Slides
- Procedure 1: Converting Continuous Data to Categorical Data
- Procedure 2: Training a Naive Bayesian Classifier
- Procedure 3: Recalling a Naive Bayesian Classifier for P
- Procedure 4: Recalling a Naive Bayesian Classifier for Classification
- Procedure 5: Create a Naive Bayesian Network with a Laplace Estimator