Lift and gain charts — lift

Create lift and gain charts for binary classification problems

lift_plot(actual, prob, positive = "yes")

Arguments

actual	actual class.
prob	predicted probability of target class.
positive	which class is the target class?

Value

a data frame with lift and gain values for each decile.

Examples

# logistic regression example
data(heart)
heart <- na.omit(heart)
fit <- glm(disease ~ . , heart, family=binomial)
pred <- predict(fit, newdata=heart, type="response")
results <- lift_plot(heart$disease, pred, positive="yes")
print(results)
#>    decile ncases nresp cumresp pctevents   gain  cumlift
#> 1      10     29    29      29     21.17  21.17 2.117000
#> 2      20     29    27      56     19.71  40.88 2.044000
#> 3      30     29    26      82     18.98  59.86 1.995333
#> 4      40     30    25     107     18.25  78.11 1.952750
#> 5      50     30    14     121     10.22  88.33 1.766600
#> 6      60     30     9     130      6.57  94.90 1.581667
#> 7      70     30     3     133      2.19  97.09 1.387000
#> 8      80     30     2     135      1.46  98.55 1.231875
#> 9      90     30     1     136      0.73  99.28 1.103111
#> 10    100     30     1     137      0.73 100.01 1.000100