Calculate Residual Drift for old model and new vs. old data

Calculate Residual Drift for old model and new vs. old data

calculate_residuals_drift(model_old, data_old, data_new, y_old, y_new,
  predict_function = predict, bins = 20)

Arguments

model_old

model created on historical / `old` data
data_old

data frame with historical / `old` data
data_new

data frame with current / `new` data
y_old

true values of target variable for historical / `old` data
y_new

true values of target variable for current / `new` data
predict_function

function that takes two arguments: model and new data and returns numeric vector with predictions, by default it's `predict`
bins

continuous variables are discretized to `bins` intervals of equal sizes

Value

an object of a class `covariate_drift` (data.frame) with Non-Intersection Distances calculated for residuals

Examples

 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)
#>                   Variable    Shift  Scaled
#>   -----------------------------------------------
#>                      floor    18.98     2.1  
#>                   no.rooms    27.33     3.1  
#>                    surface    37.96     4.3  
#>                   m2.price    23.35     2.6  
#>          construction.year    25.11     2.8  
 
 library("ranger")
 predict_function <- function(m,x,...) predict(m, x, ...)$predictions
 model_old <- ranger(m2.price ~ ., data = apartments)
 calculate_residuals_drift(model_old,
                       apartments_test[1:4000,], apartments_test[4001:8000,],
                       apartments_test$m2.price[1:4000], apartments_test$m2.price[4001:8000],
                       predict_function = predict_function)
#>                   Variable  Shift
#>   -------------------------------------
#>                  Residuals    3.7  
 calculate_residuals_drift(model_old,
                       apartments, apartments_test,
                       apartments$m2.price, apartments_test$m2.price,
                       predict_function = predict_function)
#>                   Variable  Shift
#>   -------------------------------------
#>                  Residuals   35.8  **