Print funnel_measure object
# S3 method for funnel_measure
print(x, ...)an object of class funnel_measure
other parameters
# \donttest{
library("DALEXtra")
library("mlr")
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 = 1777.927 , mean = 3504.701 , max = 6261.863
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -531.7638 , mean = 6.822683 , max = 730.1442
#> 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 = 2127.808 , mean = 3502.687 , max = 6055.905
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -524.8079 , mean = 8.836792 , max = 716.7139
#> 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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print(plot_data)
#> Funnel measure head:
#> Variable Measure Label Challenger Category
#> 1 m2.price -113.81110 [1603, 2736.8] RF Target
#> 2 m2.price -22.22151 [1603, 2736.8] GBM Target
#> 3 m2.price -139.57782 (2736.8, 3198] RF Target
#> 4 m2.price -140.66463 (2736.8, 3198] GBM Target
#> 5 m2.price -159.43259 (3198, 3634] RF Target
#> 6 m2.price -177.34656 (3198, 3634] GBM Target
#> Models Info
#> label class type
#> 1 LM WrappedModel Champion
#> 2 RF WrappedModel Challenger
#> 3 GBM WrappedModel Challenger
# }