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

In drifter: Concept Drift and Concept Shift Detection for Predictive Models

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

 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, ...)$predictions
 model_old <- ranger(m2.price ~ ., data = apartments)
 calculate_residuals_drift(model_old,
                       apartments_test[1:4000,], apartments_test[4001:8000,],
                       apartments_test$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)

drifter documentation built on May 31, 2019, 5:04 p.m.