考虑一个输入数据是5维特征值的朴素贝叶斯二元分类器,:X 有5个特征<X1,….,X5 > ,Y 的标签(分类目标)则为两个 (Y = 1 或 Y = 0). 假设计算分类标签Y时,  𝑋 𝑖 ( 𝑖 = 1 , … , 5 )   是有条件的独立的。 亦即 𝑃 ( 𝑋 𝑖 | 𝑌 = 𝑘 )   𝑁 ( 𝜇 𝑖 𝑘 ,     𝜎 𝑖 𝑘 ) ,其中, 𝑘 = 0 , 1   以及  𝑖 = 1 , … , 5   。 𝑃 ( 𝑌 )   遵循 , 𝐵 𝑒 𝑟 𝑛 𝑜 𝑢 𝑙 𝑙 𝑖 ( 𝜃 ;   1 − 𝜃 ) , 亦即 𝑃 ( 𝑌 = 0 ) = 𝜃  。 此分类器中的独立参数总数为多少?   Consider a Gaussian Naive Bayes to learn a binary classifier using 5-dimensional real-valued features: X =<X1,….,X5 > and Y class label (Y = 1 or Y = 0). Assume  𝑋 𝑖 ( 𝑖 = 1 , … , 5 )   are conditionally independent given the class label Y, i.e., 𝑃 ( 𝑋 𝑖 | 𝑌 = 𝑘 )   𝑁 ( 𝜇 𝑖 𝑘 ,     𝜎 𝑖 𝑘 )   where 𝑘 = 0 , 1   and 𝑖 = 1 , … , 5 .  𝑃 ( 𝑌 )   follows  𝐵 𝑒 𝑟 𝑛 𝑜 𝑢 𝑙 𝑙 𝑖 ( 𝜃 ;   1 − 𝜃 ) 𝑖 . 𝑒 . 𝑃 ( 𝑌 = 0 ) = 𝜃   . What is the total number of independent parameters in this classifier?Single choice

A

11

B

31

C

20

D

21

Log in for full answers

We've collected over 50,000 authentic original questions and detailed explanations from around the globe. Log in now and get instant access to the answers!

Similar Questions

More Practical Tools for Students Powered by AI Study Helper

Join us and instantly unlock extensive past papers & exclusive solutions to get a head start on your studies!