Obtaining an Auc Value from the Knn Function
I Am Using the Class Package in Order to Use the Knn Algorithm. I Am Also Using the Rocr Package to Calculate the Auc Value. Knn_One
I am using the class package in order to use the KNN algorithm. I am also using the ROCR package to calculate the AUC value.
knn_one<-knn(train, test, train$Digit, k=1)
To calculate the AUC value for another method, e.g. classification trees, I used these series of commands:
treeTrain_Pred<-predict(Tree_Train, test , type = "prob")[,2]
Pred<-prediction(treeTrain_Pred, test$Digit)
Perf<-performance(Pred, "auc")
[[1]]
However, when I try
knn_one = predict(knn_one, test, type="prob")[,2]
I get the following error:
Error in UseMethod("predict") :
no applicable method for 'predict' applied to an object of class "factor"
How can I fix this and obtain an AUC value for my KNN function?
1 Answer
There is no predict method for knn models, instead you train and receive predictions as part of a single call. Example on sonar data:
library(mlbench)
data(Sonar)
create data partition:
set.seed(1)
tr_ind <- sample(1:nrow(Sonar), 150)
train <- Sonar[tr_ind,]
test <- Sonar[-tr_ind,]
mod <- class::knn(cl = train$Class,
test = test[,1:60],
train = train[,1:60],
k = 5,
prob = TRUE)
Now the probability of the predictions are in:
attributes(mod)$prob
library(pROC)
roc(test$Class, attributes(mod)$prob)
#output
Call:
roc.default(response = test$Class, predictor = attributes(mod)$prob)
Data: attributes(mod)$prob in 30 controls (test$Class M) < 28 cases (test$Class R).
Area under the curve: 0.4667
plot(roc(test$Class, attributes(mod)$prob),
print.thres = T,
print.auc=T)
lets try with k = 4
mod <- class::knn(cl = train$Class,
test = test[,1:60],
train = train[,1:60],
k = 4,
prob = TRUE)
plot(roc(test$Class, attributes(mod)$prob),
print.thres = T,
print.auc = T,
print.auc.y = 0.2)
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