The idea of classification is to predict which category some data item belongs to. For example, does a patient have cancer (yes, no) based on the results of medical tests. Or, what category of risk is a loan application risky (low, medium, high) based on an applicant’s financial data. Naive Bayes classification is a machine-learning technique that can be used to predict to which category a particular data case belongs. Those classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem
The idea of classification is to predict which category some data item belongs to. For example, does a patient have cancer (yes, no) based on the results of medical tests. Or, what category of risk is a loan application risky (low, medium, high) based on an applicant’s financial data. Naive Bayes classification is a machine-learning technique that can be used to predict to which category a particular data case belongs. Those classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem