# Logistic Regression - how confidence is computed

Member Posts: 3 Contributor I
edited March 26 in Help
Hi everyone!

I have a question regarding rapidminer and i hope somebody can help me please : )

I've created a Workflow based on the Weka-Logistic Regression. How the coefficents are calcuated and so on is not the problem.
Furthermore I built the Workflow so, in the end it created a csv file which applies the model on my input.csv.

Now I got the confidence of yes and no, which is the probability of course (I hope so).
I was little bit  skeptic so i computed the coefficients for one row of data and i got a prob which deviates from the computed prob by rapidminer.
I was wondering, if its possible or to track how Rapdiminer computes the confidence for the data?

With kind regards
Klori
Tagged:

• Member Posts: 263 Unicorn
Upload a dataset showing the problem.

Confidences are calculated:

p = exp( X*beta ) / ( 1 + exp(X*beta) ) ,

1-p = 1/ ( 1 + exp(X*beta) )

The labels might be reversed ( the model might have been run for the 'No' class instead than the 'Yes' class.

• Member Posts: 3 Contributor I
Hi earmijo : )

Thanks for your help, but what I would like to know, if i can see how it calculates it?
For example, i see which coeffiecents it takes to calc the p?

One example, the coefficients are for yes:

score_1 -> (null)
score_2 -> -0,8628
age-> -0,2192
day -> -0,0423
time -> 0,0923
gender -> 0,3161 (binomial)
segment -> 0,6009
payment -> -0,078 (binomial)
telefon -> 0,2288 (binomial)
Constant -> -3,5047

confidence(yes) -> 1,42%
confidence(no) -> 98,58%

I was testing around and set telefon to 0, gender to 0 and I come to 1,77% yes
And if i take every coefficient it gives me 3,02%

I would very much appreciate the way to see how rpaidminer takes the coefficients and compute the p.