How we can calculate the baseline prediction in a classification problem?

r
logistic_regression

#1

I am currently solving one classification problem using logistic regression.I want to know how we can calculate baseline prediction in a classification problem just like we can find baseline prediction in a linear regression by finding the average dependent variable.

I have data in which I have to predict quality of health care .
     quality=read.csv("quality.csv")
     str(quality)
    'data.frame':   131 obs. of  14 variables:
     $ MemberID            : int  1 2 3 4 5 6 7 8 9 10 ...
     $ InpatientDays       : int  0 1 0 0 8 2 16 2 2 4 ...
     $ ERVisits            : int  0 1 0 1 2 0 1 0 1 2 ...
     $ OfficeVisits        : int  18 6 5 19 19 9 8 8 4 0 ...
     $ Narcotics           : int  1 1 3 0 3 2 1 0 3 2 ...
     $ DaysSinceLastERVisit: num  731 411 731 158 449 ...
     $ Pain                : int  10 0 10 34 10 6 4 5 5 2 ...
     $ TotalVisits         : int  18 8 5 20 29 11 25 10 7 6 ...
     $ ProviderCount       : int  21 27 16 14 24 40 19 11 28 21 ...
     $ MedicalClaims       : int  93 19 27 59 51 53 40 28 20 17 ...
     $ ClaimLines          : int  222 115 148 242 204 156 261 87 98 66 ...
     $ StartedOnCombination: logi  FALSE FALSE FALSE FALSE FALSE FALSE ...
     $ AcuteDrugGapSmall   : int  0 1 5 0 0 4 0 0 0 0 ...
     $ PoorCare            : int  0 0 0 0 0 1 0 0 1 0 ...

     table(quality$PoorCare)

     0  1 
    98 33 

In this data I want to know the baseline prediction of PoorCare.


#2

@sid100158 - You can find this by assuming all the predicted output to be of having highest frequencies.

the baseline predicted output would be 98/131

Hope this helps!

Regards,
Hinduja