# Naive Bayes Algorithm error

In the attached image is the probability with the Naives Bayes algorithm of:

Fem:dv/m/s Young own Ex-credpaid Good ->62%

I calculated the Probability so:

P(Fem:dv/m/s | Good)*P(Young | Good)*P(own | Good)*P(Ex-credpaid | good)*P(Good) -> 1/6*2/6*5/6*3/6*0.6=0,01389

I don't Know where I failed. Could someone please tell me where is my error? In this case, you are computing the probability of being good, given that the other features have a fixed value. To obtain the correct probability, you need to normalize (divide) the value from your calculation by the probability that the features have taken on those fixed values.

You can calculate this as follows: $$P(Fem:dv/m/s, Young, own, Ex-credpaid) = \\ \sum_{x \in \{good,bad\}} P(Fem:dv/m/s, Young, own, Ex-credpaid, x)$$

by the marginalization rule.

Then, by the chain rule, you may write:

$$\\ \sum_{x \in \{good,bad\}} P(Fem:dv/m/s, Young, own, Ex-credpaid | x) * P(x)$$

So the correct probability of 0.62 should be obtained by the equation:

$$\frac{P(Fem:dv/m/s, Young, own, Ex-credpaid | good) * P(good)}{\sum_{x \in \{good,bad\}} P(Fem:dv/m/s, Young, own, Ex-credpaid | x) * P(x)}$$

You just need to calculate

$$P(Fem:dv/m/s, Young, own, Ex-credpaid | bad) * P(bad)$$

and it should be easy to compute the rest.

• thanks for the Answer, but I dont have the same result of 0.62. when I doing: P(Fem:dv/m/s | bad)*P(Young | bad)*P(own | bad)*P(Ex-credpaid | bad)*P(bad) -> 2/4*3/4*1/4*2/4*0.4= 0,01875 and 0,01389/(0,01389+0,01875)=0,4255 @John Doucette Aug 20 '18 at 21:36
• Hmm. You are correct. Do you have more context for that table? Where does it come from? Aug 21 '18 at 12:23
• the table comes from this paper: (page 3) ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4909197 Aug 21 '18 at 14:05
• Hmm. It looks like there are several things at play. First, I think the authors may have used Laplacian Smoothing. This is a usual thing to do, but it changes the calculation. Basically, you add one additional observation of each kind to each of the conditional distributions to make a uniform prior, and then continue to count these when computing the posteriors. Even doing that however, I only get 50%. It's possible that the table is simply incorrect. Aug 21 '18 at 15:15
• I hear, that the probability was false, and I do the same algorithm in Python and I dont know how do you did the naivebayes algorithm, how do you did? Aug 29 '18 at 14:58