DALEX (version 0.4.9)

prediction_breakdown: Calculate Break Down Explanations

Description

This function is set deprecated. It is suggested to use break_down instead. Find information how to use these functions here: https://pbiecek.github.io/PM_VEE/breakDown.html.

Usage

prediction_breakdown(explainer, observation, ...)

Arguments

explainer

a model to be explained, preprocessed by the 'explain' function

observation

a new observarvation for which predictions need to be explained

...

other parameters that will be passed to breakDown::broken.default()

Value

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

References

Predictive Models: Visual Exploration, Explanation and Debugging https://pbiecek.github.io/PM_VEE/

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 <- prediction_breakdown(dragon_lm_explainer4, observation = new_dragon)
head(dragon_lm_predict4)
plot(dragon_lm_predict4)

# }
# NOT RUN {
library("randomForest")
dragon_rf_model4 <- randomForest(life_length ~ year_of_birth + height +
                                               weight + scars + number_of_lost_teeth,
                                 data = dragons)
dragon_rf_explainer4 <- explain(dragon_rf_model4, data = dragons, y = dragons$year_of_birth,
                                label = "model_rf")
dragon_rf_predict4 <- prediction_breakdown(dragon_rf_explainer4, observation = new_dragon)
head(dragon_rf_predict4)
plot(dragon_rf_predict4)

library("gbm")
# create a gbm model
model <- gbm(life_length ~ year_of_birth + height + weight + scars +
                           number_of_lost_teeth, data = dragons,
             distribution = "gaussian",
             n.trees = 1000,
             interaction.depth = 4,
             shrinkage = 0.01,
             n.minobsinnode = 10,
             verbose = FALSE)
 # make an explainer for the model
 explainer_gbm <- explain(model, data = dragons, predict_function =
         function(model, x) predict(model, x, n.trees = 1000))
 # create a new observation
 exp_sgn <- prediction_breakdown(explainer_gbm, observation = new_dragon)
 head(exp_sgn)
 plot(exp_sgn)

 exp_sgn <- prediction_breakdown(explainer_gbm, observation = new_dragon, baseline = 0)
 plot(exp_sgn)
 
# }
# NOT RUN {
# }