# [R] Making model predictions

Rui Barradas ru|pb@rr@d@@ @end|ng |rom @@po@pt
Sun Feb 28 12:25:46 CET 2021

```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,

Às 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 Naïve
> 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
>       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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