Function plot.funnel_measure creates funnel plot of differences in measures for two models across variable areas.
It uses data created with 'funnel_measure' function.
# S3 method for funnel_measure
plot(x, ..., dot_size = 0.5)Arguments
- x
- funnel_measure object created with
funnel_measurefunction.- ...
- other parameters
- dot_size
- size of the dot on plots. Passed to
geom_point.
Value
ggplot object
Examples
# \donttest{
library("mlr")
library("DALEXtra")
task <- mlr::makeRegrTask(
id = "R",
data = apartments,
target = "m2.price"
)
learner_lm <- mlr::makeLearner(
"regr.lm"
)
model_lm <- mlr::train(learner_lm, task)
explainer_lm <- explain_mlr(model_lm, apartmentsTest, apartmentsTest$m2.price, label = "LM")
#> Preparation of a new explainer is initiated
#> -> model label : LM
#> -> data : 9000 rows 6 cols
#> -> target variable : 9000 values
#> -> predict function : yhat.WrappedModel will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package mlr , ver. 2.19.0 , task regression ( default )
#> -> predicted values : numerical, min = 1792.597 , mean = 3506.836 , max = 6241.447
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -257.2555 , mean = 4.687686 , max = 472.356
#> A new explainer has been created!
learner_rf <- mlr::makeLearner(
"regr.ranger"
)
model_rf <- mlr::train(learner_rf, task)
explainer_rf <- explain_mlr(model_rf, apartmentsTest, apartmentsTest$m2.price, label = "RF")
#> Preparation of a new explainer is initiated
#> -> model label : RF
#> -> data : 9000 rows 6 cols
#> -> target variable : 9000 values
#> -> predict function : yhat.WrappedModel will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package mlr , ver. 2.19.0 , task regression ( default )
#> -> predicted values : numerical, min = 1794.444 , mean = 3504.864 , max = 6251.752
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -544.5636 , mean = 6.659817 , max = 749.6168
#> A new explainer has been created!
learner_gbm <- mlr::makeLearner(
"regr.gbm"
)
model_gbm <- mlr::train(learner_gbm, task)
explainer_gbm <- explain_mlr(model_gbm, apartmentsTest, apartmentsTest$m2.price, label = "GBM")
#> Preparation of a new explainer is initiated
#> -> model label : GBM
#> -> data : 9000 rows 6 cols
#> -> target variable : 9000 values
#> -> predict function : yhat.WrappedModel will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package mlr , ver. 2.19.0 , task regression ( default )
#> -> predicted values : numerical, min = 2128.788 , mean = 3501.741 , max = 6075.539
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -550.3705 , mean = 9.782555 , max = 702.9879
#> A new explainer has been created!
plot_data <- funnel_measure(explainer_lm, list(explainer_rf, explainer_gbm),
nbins = 5, measure_function = DALEX::loss_root_mean_square)
#>
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plot(plot_data)
#> $challanger_RF
#>
#> $challanger_GBM
#>
# }