Print funnel_measure object
# S3 method for training_test_comparison
print(x, ...)an object of class funnel_measure
other parameters
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 = 1788.688 , mean = 3504.824 , max = 6267.627
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -534.7233 , mean = 6.69991 , max = 770.1111
#> 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$m2.price, label = "GBM")
#> Preparation of a new explainer is initiated
#> -> model label : GBM
#> -> data : 9000 rows 6 cols
#> -> target variable : 9000 values
#> -> predict function : yhat.WrappedModel will be used ( default )
#> -> predicted values : No value for predict function target column. ( default )
#> -> model_info : package mlr , ver. 2.19.0 , task regression ( default )
#> -> predicted values : numerical, min = 2119.893 , mean = 3503.582 , max = 6080.907
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -553.073 , mean = 7.941359 , max = 680
#> A new explainer has been created!
data <- training_test_comparison(explainer_lm, list(explainer_gbm, explainer_rf),
training_data = apartments,
training_y = apartments$m2.price)
print(data)
#> Training test data head:
#> measure_test measure_train label type
#> 1 171.9012 165.51958 GBM Challenger
#> 2 150.5028 73.13922 RF Challenger
#> 3 283.0865 279.32620 LM Champion
#> Models Info
#> label class type
#> 1 LM WrappedModel Champion
#> 2 GBM WrappedModel Challenger
#> 3 RF WrappedModel Challenger