Print overall_comparison object
# S3 method for overall_comparison
print(x, ...)an object of class overall_comparison
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 = 1805.222 , mean = 3504.212 , max = 6258.903
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -545.0962 , mean = 7.311198 , max = 753.845
#> 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 = 2112.469 , mean = 3502.558 , max = 6037.706
#> -> residual function : difference between y and yhat ( default )
#> -> residuals : numerical, min = -585.5099 , mean = 8.965185 , max = 756.6632
#> A new explainer has been created!
data <- overall_comparison(explainer_lm, list(explainer_gbm, explainer_rf), type = "regression")
print(data)
#> Radar Args: 3 model_performances detected
#> Accordance table head
#> Champion Challenger Label
#> 1 4820.009 4569.153 gbm
#> 2 3292.678 3132.259 gbm
#> 3 2717.910 2679.676 gbm
#> 4 2922.751 2820.128 gbm
#> 5 2974.086 2998.472 gbm
#> 6 2527.043 2831.863 gbm
#> Models Info
#> label class type
#> 1 LM WrappedModel Champion
#> 2 gbm WrappedModel Challenger
#> 3 RF WrappedModel Challenger
# }