什么是朴素贝叶斯属性：useKernelEstimator 和 useSupervisedDiscretization
What is Naive Bayes properties: useKernelEstimator and useSupervisedDiscretization
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 'useKernelEstimator' 和 'useSupervisedDiscretization'。有人可以告诉我这两个参数是什么吗？ :)
我不是 kernelEstimator 的专家。理解最后两个。你必须先理解前两个概念。
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.
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 'useKernelEstimator' 和 'useSupervisedDiscretization'。有人可以告诉我这两个参数是什么吗？ :)
我不是 kernelEstimator 的专家。理解最后两个。你必须先理解前两个概念。
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.