DALEX (version 2.4.1)

predict_profile: 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

# NOT RUN {
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"))

# }
# NOT RUN {
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"))
 
# }
# NOT RUN {
# }