drifter (version 0.2.1)

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

Description

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

Usage

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

# NOT RUN {
 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)
 
# }
# NOT RUN {
 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$m2.price[4001:8000],
                       predict_function = predict_function)
 calculate_residuals_drift(model_old,
                       apartments, apartments_test,
                       apartments$m2.price, apartments_test$m2.price,
                       predict_function = predict_function)
# }
# NOT RUN {
# }