predict_profile: Instance Level Profile as Ceteris Paribus

In DALEX: moDel Agnostic Language for Exploration and eXplanation

Instance Level Profile as Ceteris Paribus

Description

This function calculated individual profiles aka Ceteris Paribus Profiles. From DALEX version 1.0 this function calls the ceteris_paribus from the ingredients package. Find information how to use this function here: https://ema.drwhy.ai/ceterisParibus.html.

Usage

predict_profile(
  explainer,
  new_observation,
  variables = NULL,
  ...,
  type = "ceteris_paribus",
  variable_splits_type = "uniform"
)

individual_profile(
  explainer,
  new_observation,
  variables = NULL,
  ...,
  type = "ceteris_paribus",
  variable_splits_type = "uniform"
)

Arguments

explainer	a model to be explained, preprocessed by the explain function
new_observation	a new observation for which predictions need to be explained
variables	character - names of variables to be explained
...	other parameters
type	character, currently only the ceteris_paribus is implemented
variable_splits_type	how variable grids shall be calculated? Use "quantiles" (default) for percentiles or "uniform" to get uniform grid of points. Will be passed to 'ingredients'.

Value

An object of the class ceteris_paribus_explainer. It's a data frame with calculated average response.

References

Explanatory Model Analysis. Explore, Explain, and Examine Predictive Models. https://ema.drwhy.ai/

Examples

new_dragon <- data.frame(year_of_birth = 200,
     height = 80,
     weight = 12.5,
     scars = 0,
     number_of_lost_teeth  = 5)

dragon_lm_model4 <- lm(life_length ~ year_of_birth + height +
                                     weight + scars + number_of_lost_teeth,
                       data = dragons)
dragon_lm_explainer4 <- explain(dragon_lm_model4, data = dragons, y = dragons$year_of_birth,
                                label = "model_4v")
dragon_lm_predict4 <- predict_profile(dragon_lm_explainer4,
                new_observation = new_dragon,
                variables = c("year_of_birth", "height", "scars"))
head(dragon_lm_predict4)
plot(dragon_lm_predict4,
    variables = c("year_of_birth", "height", "scars"))


library("ranger")
dragon_ranger_model4 <- ranger(life_length ~ year_of_birth + height +
                                               weight + scars + number_of_lost_teeth,
                                 data = dragons, num.trees = 50)
dragon_ranger_explainer4 <- explain(dragon_ranger_model4, data = dragons, y = dragons$year_of_birth,
                                label = "model_ranger")
dragon_ranger_predict4 <- predict_profile(dragon_ranger_explainer4,
                                           new_observation = new_dragon,
                                           variables = c("year_of_birth", "height", "scars"))
head(dragon_ranger_predict4)
plot(dragon_ranger_predict4,
    variables = c("year_of_birth", "height", "scars"))
 

DALEX documentation built on Jan. 16, 2023, 1:06 a.m.