plot.model_performance: Plot Dataset Level Model Performance Explanations

In DALEX: moDel Agnostic Language for Exploration and eXplanation

Plot Dataset Level Model Performance Explanations

Description

Plot Dataset Level Model Performance Explanations

Usage

## S3 method for class 'model_performance'
plot(
  x,
  ...,
  geom = "ecdf",
  show_outliers = 0,
  ptlabel = "name",
  lossFunction = loss_function,
  loss_function = function(x) sqrt(mean(x^2))
)

Arguments

x	a model to be explained, preprocessed by the explain function
...	other parameters
geom	either "prc", "roc", "ecdf", "boxplot", "gain", "lift" or "histogram" determines how residuals shall be summarized
show_outliers	number of largest residuals to be presented (only when geom = boxplot).
ptlabel	either "name" or "index" determines the naming convention of the outliers
lossFunction	alias for loss_function held for backwards compatibility.
loss_function	function that calculates the loss for a model based on model residuals. By default it's the root mean square. NOTE that this argument was called lossFunction.

Value

An object of the class model_performance.

Examples

 
library("ranger")
titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50,
                               probability = TRUE)
explainer_ranger  <- explain(titanic_ranger_model, data = titanic_imputed[,-8],
                             y = titanic_imputed$survived)
mp_ranger <- model_performance(explainer_ranger)
plot(mp_ranger)
plot(mp_ranger, geom = "boxplot", show_outliers = 1)

titanic_ranger_model2 <- ranger(survived~gender + fare, data = titanic_imputed,
                                num.trees = 50, probability = TRUE)
explainer_ranger2  <- explain(titanic_ranger_model2, data = titanic_imputed[,-8],
                              y = titanic_imputed$survived,
                              label = "ranger2")
mp_ranger2 <- model_performance(explainer_ranger2)
plot(mp_ranger, mp_ranger2, geom = "prc")
plot(mp_ranger, mp_ranger2, geom = "roc")
plot(mp_ranger, mp_ranger2, geom = "lift")
plot(mp_ranger, mp_ranger2, geom = "gain")
plot(mp_ranger, mp_ranger2, geom = "boxplot")
plot(mp_ranger, mp_ranger2, geom = "histogram")
plot(mp_ranger, mp_ranger2, geom = "ecdf")

titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial")
explainer_glm <- explain(titanic_glm_model, data = titanic_imputed[,-8],
                         y = titanic_imputed$survived, label = "glm",
                    predict_function = function(m,x) predict.glm(m,x,type = "response"))
mp_glm <- model_performance(explainer_glm)
plot(mp_glm)

titanic_lm_model <- lm(survived~., data = titanic_imputed)
explainer_lm <- explain(titanic_lm_model, data = titanic_imputed[,-8],
                        y = titanic_imputed$survived, label = "lm")
mp_lm <- model_performance(explainer_lm)
plot(mp_lm)

plot(mp_ranger, mp_glm, mp_lm)
plot(mp_ranger, mp_glm, mp_lm, geom = "boxplot")
plot(mp_ranger, mp_glm, mp_lm, geom = "boxplot", show_outliers = 1)

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