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ROC curves for each class of the MNIST 10-class classifier
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library(ROCR) | |
library(dplyr) | |
mnistResultsDF <- data.frame(actual = mnistTest$label, | |
fit = mnist.kknn$fit, | |
as.data.frame(mnist.kknn$prob)) | |
plotROCs <- function(df, digitList) { | |
firstPlot <- TRUE | |
legendList <- NULL | |
for (digit in digitList) { | |
dfDigit <- df %>% | |
filter(as.character(actual) == as.character(digit) | | |
as.character(fit) == as.character(digit)) %>% | |
mutate(prediction = (as.character(actual) == as.character(fit))) | |
pred <- prediction(dfDigit[,digit+3], dfDigit$prediction) | |
perf <- performance(pred, "tpr", "fpr") | |
auc <- performance(pred, "auc") | |
legendList <- append(legendList, | |
paste0("Digit: ",digit,", AUC: ", | |
round(auc@y.values[[1]], digits = 4))) | |
if (firstPlot == TRUE) { | |
plot(perf, colorize = FALSE, lty = digit+1, col = digit+1) | |
firstPlot <- FALSE | |
} else { | |
plot(perf, colorize = FALSE, add = TRUE, lty = digit+1, col = digit+1) | |
} | |
} | |
legend(x=0.4, y=0.6, | |
legend = legendList, | |
col = 1:10, | |
lty = 1:10, | |
bty = "n") | |
} | |
plotROCs(mnistResultsDF, 0:9) |
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