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

#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 .
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