DALEX (version 0.2.4)

prediction_breakdown: Explanations for a Single Prediction

Description

Explanations for a Single Prediction

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.

Examples

# NOT RUN {
library("breakDown")
new.wine <- data.frame(citric.acid = 0.35,
     sulphates = 0.6,
     alcohol = 12.5,
     pH = 3.36,
     residual.sugar = 4.8)

wine_lm_model4 <- lm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_lm_explainer4 <- explain(wine_lm_model4, data = wine, label = "model_4v")
wine_lm_predict4 <- prediction_breakdown(wine_lm_explainer4, observation = new.wine)
wine_lm_predict4
plot(wine_lm_predict4)

# }
# NOT RUN {
library("randomForest")
wine_rf_model4 <- randomForest(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine)
wine_rf_explainer4 <- explain(wine_rf_model4, data = wine, label = "model_rf")
wine_rf_predict4 <- prediction_breakdown(wine_rf_explainer4, observation = new.wine)
wine_rf_predict4
plot(wine_rf_predict4)

library("gbm")
# create a gbm model
model <- gbm(quality ~ pH + residual.sugar + sulphates + alcohol, data = wine,
             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 = wine, predict_function =
         function(model, x) predict(model, x, n.trees = 1000))
 # create a new observation
 exp_sgn <- prediction_breakdown(explainer_gbm, observation = new.wine)
 exp_sgn
 plot(exp_sgn)
 
# }
# NOT RUN {
# }