Confusion Counts - R Programming

Classification evaluation compares predicted labels with actual labels. A probability threshold changes those counts.
Program

Play the script to change the threshold and see true-positive and false-positive counts.
                            threshold
                            threshold = 0.6 (default)threshold = 0.5threshold = 0.7
                        

                            confusion_counts.R
                        
Replay: real traced execution (multi-file project)

                            actual <- c("yes", "no", "yes", "yes", "no", "no")
prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)
threshold <- 0.6
predicted <- ifelse(prob >= threshold, "yes", "no")
tp <- sum(actual == "yes" & predicted == "yes")
fp <- sum(actual == "no" & predicted == "yes")
label <- paste(tp, fp, sep = "/")
cat(label, "\n", sep = "")
actual <- c("yes", "no", "yes", "yes", "no", "no")
prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)
threshold <- 0.5
predicted <- ifelse(prob >= threshold, "yes", "no")
tp <- sum(actual == "yes" & predicted == "yes")
fp <- sum(actual == "no" & predicted == "yes")
label <- paste(tp, fp, sep = "/")
cat(label, "\n", sep = "")
actual <- c("yes", "no", "yes", "yes", "no", "no")
prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)
threshold <- 0.7
predicted <- ifelse(prob >= threshold, "yes", "no")
tp <- sum(actual == "yes" & predicted == "yes")
fp <- sum(actual == "no" & predicted == "yes")
label <- paste(tp, fp, sep = "/")
cat(label, "\n", sep = "")

                        
1actual ← yes, no, yes, yes, no, no1actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)⋯values this stepyes, no, yes, yes, no, noactual
2prob ← 0.90, 0.40, 0.65, 0.55, 0.30, 0.801actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.6⋯values this step0.90, 0.40, 0.65, 0.55, 0.30, 0.80prob
3threshold ← 0.6⋯2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.64predicted <- ifelse(prob >= threshold, "yes", "no")⋯values this step0.6threshold
4predicted ← yes, no, yes, no, no, yes⋯3threshold <- 0.64predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")⋯values this stepyes, no, yes, no, no, yespredicted6 scoresprob0.6threshold
5tp ← 2⋯4predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")⋯values this step2tp3 yesactual3 yespredicted
6fp ← 1⋯5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")⋯values this step1fp3 noactual3 yespredicted
7label ← 2/1⋯6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")values this step2/1label2tp1fp
8cat(label, " ", sep = "")⋯7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")output2/1values this step2/1label
1actual ← yes, no, yes, yes, no, no1actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)⋯values this stepyes, no, yes, yes, no, noactual
2prob ← 0.90, 0.40, 0.65, 0.55, 0.30, 0.801actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.5⋯values this step0.90, 0.40, 0.65, 0.55, 0.30, 0.80prob
3threshold ← 0.5⋯2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.54predicted <- ifelse(prob >= threshold, "yes", "no")⋯values this step0.5threshold
4predicted ← yes, no, yes, yes, no, yes⋯3threshold <- 0.54predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")⋯values this stepyes, no, yes, yes, no, yespredicted6 scoresprob0.5threshold
5tp ← 3⋯4predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")⋯values this step3tp3 yesactual4 yespredicted
6fp ← 1⋯5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")⋯values this step1fp3 noactual4 yespredicted
7label ← 3/1⋯6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")values this step3/1label3tp1fp
8cat(label, " ", sep = "")⋯7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")output3/1values this step3/1label
1actual ← yes, no, yes, yes, no, no1actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)⋯values this stepyes, no, yes, yes, no, noactual
2prob ← 0.90, 0.40, 0.65, 0.55, 0.30, 0.801actual <- c("yes", "no", "yes", "yes", "no", "no")2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.7⋯values this step0.90, 0.40, 0.65, 0.55, 0.30, 0.80prob
3threshold ← 0.7⋯2prob <- c(0.90, 0.40, 0.65, 0.55, 0.30, 0.80)3threshold <- 0.74predicted <- ifelse(prob >= threshold, "yes", "no")⋯values this step0.7threshold
4predicted ← yes, no, no, no, no, yes⋯3threshold <- 0.74predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")⋯values this stepyes, no, no, no, no, yespredicted6 scoresprob0.7threshold
5tp ← 1⋯4predicted <- ifelse(prob >= threshold, "yes", "no")5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")⋯values this step1tp3 yesactual2 yespredicted
6fp ← 1⋯5tp <- sum(actual == "yes" & predicted == "yes")6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")⋯values this step1fp3 noactual2 yespredicted
7label ← 1/1⋯6fp <- sum(actual == "no" & predicted == "yes")7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")values this step1/1label1tp1fp
8cat(label, " ", sep = "")⋯7label <- paste(tp, fp, sep = "/")8cat(label, "\n", sep = "")output1/1values this step1/1label
threshold `prob >= threshold` turns probabilities into predicted labels.
true positive `tp` counts rows where actual and predicted labels are both `yes`.
false positive `fp` counts `no` rows predicted as `yes`.