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I was doing a logistic regression and made a table that represents the predicted probability ,actual class, and predicted class. If the predicted probability is more than 0.5, I classified it as 1,so the predicted class becomes 1. But I want to change the threshold value from 0.5 to another value.

I was considering to find a threshold value that maximizes both true positive rate and true negative rate. Here I made a simple data df to demonstrate what I want to do.

df<-data.frame(actual_class=c(0,1,0,0,1,1,1,0,0,1),
               predicted_probability=c(0.51,0.3,0.2,0.35,0.78,0.69,0.81,0.31,0.59,0.12),
               predicted_class=c(1,0,0,0,1,1,1,0,1,0))

If I can find a threshold value, I will classify using that value instead of 0.5. I don't know how to find a threshold value that both maximizes true positive rate and true negative rate.

Lee
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1 Answers1

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You can check a range of values pretty easily:

probs <- seq(0, 1, by=.05)
names(probs) <- probs
results <- sapply(probs, function(x) df$actual_class == as.integer(df$predicted_probability > x))

results is a 10 row by 21 column logical matrix showing when the predicted class equals the actual class:

colSums(results)   # Number of correct predictions
   0 0.05  0.1 0.15  0.2 0.25  0.3 0.35  0.4 0.45  0.5 0.55  0.6 0.65  0.7 0.75  0.8 0.85  0.9 0.95    1 
   5    5    5    4    5    5    4    6    6    6    6    7    8    8    7    7    6    5    5    5    5 
predict <- as.integer(df$predicted_probability > .6)
xtabs(~df$actual_class+predict)
#                predict
# df$actual_class 0 1
#               0 5 0
#               1 2 3

You can see that probabilities of .6 and .65 result in 8 correct predictions. This conclusion is based on the data you used in the analysis so it probably overestimates how successful you would be with new data.

dcarlson
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