Question

We need to build a model for a category Y for a binary classification problem (with categories of: 0,1) and having one binary attribute X1 (with possible values of: 0,1 per attribute).

The Naïve Bayes algorithm was chosen for training the classification model.

After the training phase, we got the following results:

p(X1=0|Y=0)=0.35, p(X1=1|Y=0)=0.65
p(X1=0|Y=1)=0.3, p(X1=1|Y=1)=0.7
p(Y=0)=0.5, p(Y=1)=0.5

Which of the following answers is correct?

Select the best answer

Select one:
A.
If the category Y=1, we will classify the example as X1=1
B.
If the value of X1 is 0 we will classify the example as category Y=0
C.
If the value of X1 is 1 we will classify the example as category Y=0
D.
None of the answers is correct, since we're missing the probabilities: p(X1=0), p(X1=1),

Answer 1

D. None of the answers is correct, since we're missing the probabilities: p(X1=0), p(X1=1),

Step-by-step explanation:

To calculate the probability that an example belongs to a particular category, we need to know the prior probability of the category (p(Y=0) or p(Y=1)), the probability of the attribute value given the category (p(X1=0|Y=0) or p(X1=1|Y=1)), and the probability of the attribute value (p(X1=0) or p(X1=1)). We are missing the latter two probabilities, so we cannot calculate the probability that an example belongs to a particular category.