Plot Dataset Level Model Profile Explanations

# S3 method for model_profile
plot(x, ..., geom = "aggregates")

Arguments

x

a variable profile explanation, created with the model_profile function

...

other parameters

geom

either "aggregates", "profiles", "points" determines which will be plotted

Value

An object of the class ggplot.

aggregates

  • color a character. Either name of a color, or hex code for a color, or _label_ if models shall be colored, or _ids_ if instances shall be colored

  • size a numeric. Size of lines to be plotted

  • alpha a numeric between 0 and 1. Opacity of lines

  • facet_ncol number of columns for the facet_wrap

  • variables if not NULL then only variables will be presented

  • title a character. Partial and accumulated dependence explainers have deafult value.

  • subtitle a character. If NULL value will be dependent on model usage.

Examples

titanic_glm_model <- glm(survived~., data = titanic_imputed, family = "binomial") explainer_glm <- explain(titanic_glm_model, data = titanic_imputed)
#> Preparation of a new explainer is initiated #> -> model label : lm ( default ) #> -> data : 2207 rows 8 cols #> -> target variable : not specified! ( WARNING ) #> -> predict function : yhat.glm will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package stats , ver. 4.1.1 , task classification ( default ) #> -> model_info : Model info detected classification task but 'y' is a NULL . ( WARNING ) #> -> model_info : By deafult classification tasks supports only numercical 'y' parameter. #> -> model_info : Consider changing to numerical vector with 0 and 1 values. #> -> model_info : Otherwise I will not be able to calculate residuals or loss function. #> -> predicted values : numerical, min = 0.008128381 , mean = 0.3221568 , max = 0.9731431 #> -> residual function : difference between y and yhat ( default ) #> A new explainer has been created!
expl_glm <- model_profile(explainer_glm, "fare") plot(expl_glm)
# \donttest{ library("ranger") titanic_ranger_model <- ranger(survived~., data = titanic_imputed, num.trees = 50, probability = TRUE) explainer_ranger <- explain(titanic_ranger_model, data = titanic_imputed)
#> Preparation of a new explainer is initiated #> -> model label : ranger ( default ) #> -> data : 2207 rows 8 cols #> -> target variable : not specified! ( WARNING ) #> -> predict function : yhat.ranger will be used ( default ) #> -> predicted values : No value for predict function target column. ( default ) #> -> model_info : package ranger , ver. 0.13.1 , task classification ( default ) #> -> model_info : Model info detected classification task but 'y' is a NULL . ( WARNING ) #> -> model_info : By deafult classification tasks supports only numercical 'y' parameter. #> -> model_info : Consider changing to numerical vector with 0 and 1 values. #> -> model_info : Otherwise I will not be able to calculate residuals or loss function. #> -> predicted values : numerical, min = 0.007755556 , mean = 0.3218902 , max = 0.9938384 #> -> residual function : difference between y and yhat ( default ) #> A new explainer has been created!
expl_ranger <- model_profile(explainer_ranger) plot(expl_ranger)
plot(expl_ranger, geom = "aggregates")
vp_ra <- model_profile(explainer_ranger, type = "partial", variables = c("age", "fare")) plot(vp_ra, variables = c("age", "fare"), geom = "points")
vp_ra <- model_profile(explainer_ranger, type = "partial", k = 3) plot(vp_ra)
plot(vp_ra, geom = "profiles")
plot(vp_ra, geom = "points")
vp_ra <- model_profile(explainer_ranger, type = "partial", groups = "gender") plot(vp_ra)
plot(vp_ra, geom = "profiles")
plot(vp_ra, geom = "points")
vp_ra <- model_profile(explainer_ranger, type = "accumulated") plot(vp_ra)
plot(vp_ra, geom = "profiles")
plot(vp_ra, geom = "points")
# }