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

 1 2 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

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 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.