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caret package confusion matrix define positive case with multiple classes

By : Don VJ
Date : October 18 2020, 08:10 PM
Does that help Looks like a misinterpretation. It happens that positive is not used anywhere when there are more than two classes. First caret:::confusionMatrix.default gets called for some "formalities" and then we go to caret:::confusionMatrix.table. There positive gets used multiple times when there are two classes, but nothing outside of that if case.
As you said, it's not hard to achieve that by hand. For a quick glance you may use simply
code :
table(data.frame(data == "A"))
#         modeled
# measured FALSE TRUE
#    FALSE    30   40
#    TRUE      0   30

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R how to visualize confusion matrix using the caret package

By : Firmu
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , I'd like to visualize the data I've put in the confusion matrix. Is there a function I could simply put the confusion matrix and it would visualize it (plot it)? , You could use the built-in fourfoldplot. For example,
code :
ctable <- as.table(matrix(c(42, 6, 8, 28), nrow = 2, byrow = TRUE))
fourfoldplot(ctable, color = c("#CC6666", "#99CC99"),
             conf.level = 0, margin = 1, main = "Confusion Matrix")

How to load already computed confusion matrix to be used with the caret package in R

By : phynyrd
Date : March 29 2020, 07:55 AM
around this issue to Sowmya's comments, I achieve it by using the following code:
code :
confusionMatrix(clf$predicted, labels)

Confusion Matrix and caret package - rpart algorithm

By : mcihc ba
Date : March 29 2020, 07:55 AM
hop of those help? I have just found out the reason why my confusionMatrix was nbot working properly. This was because I was loading library(caret) as well as library(ModelMetrics) at the start of the code. as soon as I commented out library(ModelMetrics) and retarted my R, I was able to get the Confusion Matrix working perfectly fine.

Confusion matrix when all classes are not predicted with caret package in R

By : Jianbin Hong
Date : March 29 2020, 07:55 AM
help you fix your problem I have a classification model predict(model, test.x) to evaluate a model on a data with 11 classes, the result of prediction: , Make the levels same by joining the factors with union
code :
all_class <- union(predicted_class, test.y)
newtable <- table(factor(predicted_class, all_class), factor(test.y, all_class))

Caret package - defining Positive result

By : Miss Katherine
Date : March 29 2020, 07:55 AM
seems to work fine look at this example. Extended this from the caret examples with confusionMatrix.
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