Às 11:12 de 22/09/2023, Milbert, Sabine (LGL) escreveu:
Dear R Help Team,

My research group and I use R scripts for our multivariate data screening 
routines. During routine use, we encountered some inconsistencies within the 
predict() function of the R Stats Package. Through internal research, we were 
unable to find the reason for this and have decided to contact your help team 
with the following issue:

The predict() function is used once to predict the class membership of a new sample (type = 
"class") on a trained linear SVM model for distinguishing two classes (using the caret 
package). It is then used to also examine the probability of class membership (type = 
"prob"). Both are then presented in an R shiny output. Within the routine, we noticed two 
samples (out of 100+) where the class prediction and probability prediction did not match. The 
prediction probabilities of one class (52%) did not match the class membership within the predict 
function. We use the same seed and the discrepancy is reproducible in this sample. The same problem 
did not occur in other trained models (lda, random forest, radial SVM...).

Is there a weighing of classes within the prediction function or is the 
classification limit not at 50%/a majority vote? Or do you have another 
explanation for this discrepancy, please let us know.

PS: If this is an issue based on the model training function of the caret 
package and therefore not your responsibility, please let us know.

Thank you in advance for your support!

Yours sincerely,
Sabine Milbert

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Hello,

I cannot tell what is going on but I would like to make a correction to your post.

predict() is a generic function with methods for objects of several classes in many packages. In base package stats you will find methods for objects (fits) of class lm, glm and others, see ?predict.

The method you are asking about is predict.train, defined in package caret, not in package stats.
to see what predict method is being called, check


class(your_fit)


Hope this helps,

Rui Barradas

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