algorithm - 什么是朴素贝叶斯属性 : useKernelEstimator and useSupervisedDiscretization

Question

我在 Weka 数据挖掘工具中使用朴素贝叶斯作为学习算法。朴素贝叶斯中有参数选项，如“useKernelEstimator”和“useSupervisedDiscretization”。有人可以告诉我这两个参数是什么吗？ :)

Answer 1

我不是 kernelEstimator 的专家。理解最后两个。您必须先了解前两个概念。

KernelEstimator: the precision to which numeric values are given. For example, if the precision is stated to be 0.1, the values in the interval (0.25,0.35] are all treated as 0.3.

Supervised Discretization Once in a while one has numeric data but wants to use classifier that handles only nominal values. In that case one needs to discretize the data

useKernelEstimator -- Use a kernel estimator for numeric attributes rather than a normal distribution.

useSupervisedDiscretization -- Use supervised discretization to convert numeric attributes to nominal ones.