Description Usage Arguments Value Examples
Print Model Drift Data Frame
1 2 |
x |
an object of the class 'model_drift' |
max_length |
length of the first column, by default 25 |
... |
other arguments, currently ignored |
this function prints a data frame with a nicer format
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | library("DALEX")
model_old <- lm(m2.price ~ ., data = apartments)
model_new <- lm(m2.price ~ ., data = apartments_test[1:1000,])
calculate_model_drift(model_old, model_new,
apartments_test[1:1000,],
apartments_test[1:1000,]$m2.price)
library("ranger")
predict_function <- function(m,x,...) predict(m, x, ...)$predictions
model_old <- ranger(m2.price ~ ., data = apartments)
model_new <- ranger(m2.price ~ ., data = apartments_test)
calculate_model_drift(model_old, model_new,
apartments_test,
apartments_test$m2.price,
predict_function = predict_function)
# here we compare model created on male data
# with model applied to female data
# there is interaction with age, and it is detected here
predict_function <- function(m,x,...) predict(m, x, ..., probability=TRUE)$predictions[,1]
data_old = HR[HR$gender == "male", -1]
data_new = HR[HR$gender == "female", -1]
model_old <- ranger(status ~ ., data = data_old, probability=TRUE)
model_new <- ranger(status ~ ., data = data_new, probability=TRUE)
calculate_model_drift(model_old, model_new,
HR_test,
HR_test$status == "fired",
predict_function = predict_function)
# plot it
library("ingredients")
prof_old <- partial_dependency(model_old,
data = data_new[1:1000,],
label = "model_old",
predict_function = predict_function,
grid_points = 101,
variable_splits = NULL)
prof_new <- partial_dependency(model_new,
data = data_new[1:1000,],
label = "model_new",
predict_function = predict_function,
grid_points = 101,
variable_splits = NULL)
plot(prof_old, prof_new, color = "_label_")
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