check_drift: This function executes all tests for drift between two...
In drifter: Concept Drift and Concept Shift Detection for Predictive Models

Description Usage Arguments Value Examples

Currently three checks are implemented, covariate drift, residual drift and model drift.

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check_drift(model_old, model_new, data_old, data_new, y_old, y_new,
  predict_function = predict, max_obs = 100, bins = 20,
  scale = sd(y_new, na.rm = TRUE))

`model_old`	model created on historical / 'old'data
`model_new`	model created on current / 'new'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'
`max_obs`	if negative, them all observations are used for calculation of PDP, is positive, then only 'max_obs' are used for calculation of PDP
`bins`	continuous variables are discretized to 'bins' intervals of equal sizes
`scale`	scale parameter for calculation of scaled drift

This function is executed for its side effects, all checks are being printed on the screen. Additionaly it returns list with particualr checks.

 library("DALEX")
 model_old <- lm(m2.price ~ ., data = apartments)
 model_new <- lm(m2.price ~ ., data = apartments_test[1:1000,])
 check_drift(model_old, model_new,
                  apartments, apartments_test,
                  apartments$m2.price, apartments_test$m2.price)
 
 library("ranger")
 predict_function <- function(m,x,...) predict(m, x, ...)$predictions
 model_old <- ranger(m2.price ~ ., data = apartments)
 model_new <- ranger(m2.price ~ ., data = apartments_test)
 check_drift(model_old, model_new,
                  apartments, apartments_test,
                  apartments$m2.price, apartments_test$m2.price,
                  predict_function = predict_function)