Making model predictions
Rui Actually yes. I was able to work this into my shiny app this afternoon. Thank you Jeff -----Original Message----- From: Rui Barradas <ruipbarradas at sapo.pt> Sent: Sunday, February 28, 2021 5:26 AM To: reichmanj at sbcglobal.net; R-help at r-project.org Subject: Re: [R] Making model predictions Hello, Are you looking for this? newd <- data.frame( Class = '1st', Sex = 'Male', Age = 'Child' ) predict(m, newdata = newd, type = 'raw') # No Yes #[1,] 0.3169345 0.6830655 With the default type = 'class' the result is predict(m, newdata = newd) #[1] Yes #Levels: No Yes Hope this helps, Rui Barradas ?s 14:42 de 27/02/21, Jeff Reichman escreveu:
R User Forum
Is there a better way than grabbing individual cell values from a
model output to make predictions. For example the output from the
following Na?ve Bayes model
library(e1071)
## Example of using a contingency table:
data(Titanic)
m <- naiveBayes(Survived ~ ., data = Titanic) m
will produce the following results:
Call:
naiveBayes.formula(formula = Survived ~ ., data = Titanic)
A-priori probabilities:
Survived
No Yes
0.676965 0.323035
Conditional probabilities:
Class
Survived 1st 2nd 3rd Crew
No 0.08187919 0.11208054 0.35436242 0.45167785
Yes 0.28551336 0.16596343 0.25035162 0.29817159
Sex
Survived Male Female
No 0.91543624 0.08456376
Yes 0.51617440 0.48382560
Age
Survived Child Adult
No 0.03489933 0.96510067
Yes 0.08016878 0.91983122
Say I want to calculate the probability of P(survival = No | Class =
1st, Sex = Male, and Age= Child).
While I can set an object (e.g. myObj <- m$tables$Class[1,1]) to the
respective cell and perform the calculation, there must be a better
way, as I continue to learn R.
Jeff
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