R Under development (unstable) (2026-08-17 r90424 ucrt) -- "Unsuffered Consequences" Copyright (C) 2026 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > library(awe) > > test_check("awe") == 1/7 Explore == AWE Exploratory Summary Rows: 32 | Columns: 11 Numeric variables: 11 | Categorical variables: 0 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis mpg 20.091 6.027 10.400 15.425 19.200 22.80 33.900 0.611 -0.373 cyl 6.188 1.786 4.000 4.000 6.000 8.00 8.000 -0.175 -1.762 disp 230.722 123.939 71.100 120.825 196.300 326.00 472.000 0.382 -1.207 hp 146.688 68.563 52.000 96.500 123.000 180.00 335.000 0.726 -0.136 drat 3.597 0.535 2.760 3.080 3.695 3.92 4.930 0.266 -0.715 wt 3.217 0.978 1.513 2.581 3.325 3.61 5.424 0.423 -0.023 qsec 17.849 1.787 14.500 16.892 17.710 18.90 22.900 0.369 0.335 vs 0.438 0.504 0.000 0.000 0.000 1.00 1.000 0.240 -2.002 am 0.406 0.499 0.000 0.000 0.000 1.00 1.000 0.364 -1.925 gear 3.688 0.738 3.000 3.000 4.000 4.00 5.000 0.529 -1.070 carb 2.812 1.615 1.000 2.000 2.000 4.00 8.000 1.051 1.257 n_outliers mpg 1 cyl 0 disp 0 hp 1 drat 0 wt 3 qsec 1 vs 0 am 0 gear 0 carb 1 == 2/7 Preprocess == Train: 24 rows | Test: 8 rows == 3/7 Select features == Skipped (select_features = FALSE) == 4/7 Model == AWE Model Selection Target: mpg (continuous) Candidates tried: 15 Leaderboard: model metric mean_score sd_score gradient_boosting RMSE 2.794120 0.5172574 ranger RMSE 2.849956 0.5637010 random_forest RMSE 2.880504 0.5093567 mars RMSE 3.245521 0.3023847 xgboost RMSE 3.269358 0.5532934 knn_regression RMSE 3.695095 0.4455940 ridge_regression RMSE 3.825165 0.9555778 svm_radial RMSE 3.900662 1.2490972 elastic_net RMSE 4.087515 1.5489662 lasso_regression RMSE 4.410835 2.0204418 svm_linear RMSE 5.295902 2.3660430 pls_regression RMSE 5.808063 3.5214919 decision_tree RMSE 6.158014 1.1204867 robust_regression RMSE 7.346871 4.3613038 linear_regression RMSE 7.399377 5.2582285 Best model: gradient_boosting == 5/7 Wrap == AWE Wrapper Model: gradient_boosting Target: mpg (continuous) Predictors: 10 Class probabilities available: FALSE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance hp 2.14677454 wt 1.14182402 disp 0.84542093 qsec 0.46541751 drat 0.25168646 cyl 0.21803403 am 0.02139715 vs 0.00000000 gear 0.00000000 carb 0.00000000 == 7/7 Evaluate == AWE Evaluation RMSE: 1.8639 | MAE: 1.3358 | MAPE: 7.23% | R2: 0.9249 == 1/7 Explore == AWE Exploratory Summary Rows: 100 | Columns: 5 Numeric variables: 4 | Categorical variables: 1 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis Sepal.Length 6.262 0.663 4.9 5.800 6.3 6.700 7.9 0.299 -0.197 Sepal.Width 2.872 0.333 2.0 2.700 2.9 3.025 3.8 0.036 0.342 Petal.Length 4.906 0.826 3.0 4.375 4.9 5.525 6.9 0.166 -0.421 Petal.Width 1.676 0.425 1.0 1.300 1.6 2.000 2.5 0.226 -1.068 n_outliers Sepal.Length 0 Sepal.Width 7 Petal.Length 0 Petal.Width 0 == 2/7 Preprocess == Train: 76 rows | Test: 24 rows == 3/7 Select features == Skipped (select_features = FALSE) == 4/7 Model == AWE Model Selection Target: Species (binary) Candidates tried: 15 Leaderboard: model metric mean_score sd_score lda Accuracy 1.0000000 0.0000000000 knn Accuracy 1.0000000 0.0000000000 qda Accuracy 0.9871795 0.0222057796 neural_net Accuracy 0.9866667 0.0230940108 random_forest Accuracy 0.9738462 0.0226629536 svm_radial Accuracy 0.9738462 0.0226629536 svm_linear Accuracy 0.9738462 0.0226629536 svm_kernlab Accuracy 0.9738462 0.0226629536 regularized_logistic Accuracy 0.9733333 0.0230940108 logistic_or_multinomial Accuracy 0.9605128 0.0008882312 ranger Accuracy 0.9605128 0.0008882312 ranger_prob Accuracy 0.9605128 0.0008882312 naive_bayes Accuracy 0.9605128 0.0008882312 decision_tree Accuracy 0.9471795 0.0235506923 gradient_boosting Accuracy 0.9471795 0.0235506923 Best model: lda == 5/7 Wrap == AWE Wrapper Model: lda Target: Species (binary) Predictors: 4 Class probabilities available: TRUE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance Petal.Length 0.37105263 Petal.Width 0.37105263 Sepal.Length 0.04210526 Sepal.Width 0.02105263 == 7/7 Evaluate == AWE Evaluation Accuracy: 0.8333 | Macro-Precision: 0.8429 | Macro-Recall: 0.8333 | Macro-F1: 0.8322 Confusion matrix: Predicted Actual versicolor virginica versicolor 9 3 virginica 1 11 == 1/7 Explore == AWE Exploratory Summary Rows: 32 | Columns: 11 Numeric variables: 11 | Categorical variables: 0 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis mpg 20.091 6.027 10.400 15.425 19.200 22.80 33.900 0.611 -0.373 cyl 6.188 1.786 4.000 4.000 6.000 8.00 8.000 -0.175 -1.762 disp 230.722 123.939 71.100 120.825 196.300 326.00 472.000 0.382 -1.207 hp 146.688 68.563 52.000 96.500 123.000 180.00 335.000 0.726 -0.136 drat 3.597 0.535 2.760 3.080 3.695 3.92 4.930 0.266 -0.715 wt 3.217 0.978 1.513 2.581 3.325 3.61 5.424 0.423 -0.023 qsec 17.849 1.787 14.500 16.892 17.710 18.90 22.900 0.369 0.335 vs 0.438 0.504 0.000 0.000 0.000 1.00 1.000 0.240 -2.002 am 0.406 0.499 0.000 0.000 0.000 1.00 1.000 0.364 -1.925 gear 3.688 0.738 3.000 3.000 4.000 4.00 5.000 0.529 -1.070 carb 2.812 1.615 1.000 2.000 2.000 4.00 8.000 1.051 1.257 n_outliers mpg 1 cyl 0 disp 0 hp 1 drat 0 wt 3 qsec 1 vs 0 am 0 gear 0 carb 1 == 2/7 Preprocess == Train: 24 rows | Test: 8 rows == 3/7 Select features == Skipped (select_features = FALSE) == 4/7 Model == AWE Model Selection Target: mpg (continuous) Candidates tried: 15 Leaderboard: model metric mean_score sd_score gradient_boosting RMSE 2.794120 0.5172574 ranger RMSE 2.849956 0.5637010 random_forest RMSE 2.880504 0.5093567 mars RMSE 3.245521 0.3023847 xgboost RMSE 3.269358 0.5532934 knn_regression RMSE 3.695095 0.4455940 ridge_regression RMSE 3.825165 0.9555778 svm_radial RMSE 3.900662 1.2490972 elastic_net RMSE 4.087515 1.5489662 lasso_regression RMSE 4.410835 2.0204418 svm_linear RMSE 5.295902 2.3660430 pls_regression RMSE 5.808063 3.5214919 decision_tree RMSE 6.158014 1.1204867 robust_regression RMSE 7.346871 4.3613038 linear_regression RMSE 7.399377 5.2582285 Best model: gradient_boosting == 5/7 Wrap == AWE Wrapper Model: gradient_boosting Target: mpg (continuous) Predictors: 10 Class probabilities available: FALSE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance hp 2.14677454 wt 1.14182402 disp 0.84542093 qsec 0.46541751 drat 0.25168646 cyl 0.21803403 am 0.02139715 vs 0.00000000 gear 0.00000000 carb 0.00000000 == 7/7 Evaluate == AWE Evaluation RMSE: 1.8639 | MAE: 1.3358 | MAPE: 7.23% | R2: 0.9249 == 1/7 Explore == AWE Exploratory Summary Rows: 150 | Columns: 5 Numeric variables: 4 | Categorical variables: 1 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis n_outliers Sepal.Length 5.843 0.828 4.3 5.1 5.80 6.4 7.9 0.309 -0.606 0 Sepal.Width 3.057 0.436 2.0 2.8 3.00 3.3 4.4 0.313 0.139 4 Petal.Length 3.758 1.765 1.0 1.6 4.35 5.1 6.9 -0.269 -1.417 0 Petal.Width 1.199 0.762 0.1 0.3 1.30 1.8 2.5 -0.101 -1.358 0 == 2/7 Preprocess == Train: 114 rows | Test: 36 rows == 3/7 Select features == Skipped (select_features = FALSE) == 4/7 Model == AWE Model Selection Target: Species (multiclass) Candidates tried: 14 Leaderboard: model metric mean_score sd_score qda Accuracy 1.0000000 0.00000000 lda Accuracy 0.9912281 0.01519343 svm_kernlab Accuracy 0.9824561 0.01519343 regularized_logistic Accuracy 0.9824561 0.03038686 svm_linear Accuracy 0.9824561 0.03038686 logistic_or_multinomial Accuracy 0.9736842 0.02631579 random_forest Accuracy 0.9736842 0.02631579 ranger Accuracy 0.9736842 0.02631579 ranger_prob Accuracy 0.9736842 0.02631579 neural_net Accuracy 0.9736842 0.02631579 svm_radial Accuracy 0.9736842 0.02631579 decision_tree Accuracy 0.9649123 0.03038686 naive_bayes Accuracy 0.9649123 0.01519343 knn Accuracy 0.9649123 0.01519343 Best model: qda == 5/7 Wrap == AWE Wrapper Model: qda Target: Species (multiclass) Predictors: 4 Class probabilities available: TRUE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance Petal.Length 0.42456140 Petal.Width 0.37894737 Sepal.Length 0.02105263 Sepal.Width 0.01052632 == 7/7 Evaluate == AWE Evaluation Accuracy: 0.9444 | Macro-Precision: 0.9524 | Macro-Recall: 0.9444 | Macro-F1: 0.9441 Confusion matrix: Predicted Actual setosa versicolor virginica setosa 12 0 0 versicolor 0 10 2 virginica 0 0 12 == 1/7 Explore == AWE Exploratory Summary Rows: 32 | Columns: 11 Numeric variables: 11 | Categorical variables: 0 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis mpg 20.091 6.027 10.400 15.425 19.200 22.80 33.900 0.611 -0.373 cyl 6.188 1.786 4.000 4.000 6.000 8.00 8.000 -0.175 -1.762 disp 230.722 123.939 71.100 120.825 196.300 326.00 472.000 0.382 -1.207 hp 146.688 68.563 52.000 96.500 123.000 180.00 335.000 0.726 -0.136 drat 3.597 0.535 2.760 3.080 3.695 3.92 4.930 0.266 -0.715 wt 3.217 0.978 1.513 2.581 3.325 3.61 5.424 0.423 -0.023 qsec 17.849 1.787 14.500 16.892 17.710 18.90 22.900 0.369 0.335 vs 0.438 0.504 0.000 0.000 0.000 1.00 1.000 0.240 -2.002 am 0.406 0.499 0.000 0.000 0.000 1.00 1.000 0.364 -1.925 gear 3.688 0.738 3.000 3.000 4.000 4.00 5.000 0.529 -1.070 carb 2.812 1.615 1.000 2.000 2.000 4.00 8.000 1.051 1.257 n_outliers mpg 1 cyl 0 disp 0 hp 1 drat 0 wt 3 qsec 1 vs 0 am 0 gear 0 carb 1 == 2/7 Preprocess == Train: 24 rows | Test: 8 rows == 3/7 Select features == Skipped (select_features = FALSE) == 4/7 Model == AWE Model Selection Target: mpg (continuous) Candidates tried: 15 Leaderboard: model metric mean_score sd_score gradient_boosting RMSE 2.794120 0.5172574 ranger RMSE 2.849956 0.5637010 random_forest RMSE 2.880504 0.5093567 mars RMSE 3.245521 0.3023847 xgboost RMSE 3.269358 0.5532934 knn_regression RMSE 3.695095 0.4455940 ridge_regression RMSE 3.825165 0.9555778 svm_radial RMSE 3.900662 1.2490972 elastic_net RMSE 4.087515 1.5489662 lasso_regression RMSE 4.410835 2.0204418 svm_linear RMSE 5.295902 2.3660430 pls_regression RMSE 5.808063 3.5214919 decision_tree RMSE 6.158014 1.1204867 robust_regression RMSE 7.346871 4.3613038 linear_regression RMSE 7.399377 5.2582285 Best model: gradient_boosting == 5/7 Wrap == AWE Wrapper Model: gradient_boosting Target: mpg (continuous) Predictors: 10 Class probabilities available: FALSE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance hp 2.14677454 wt 1.14182402 disp 0.84542093 qsec 0.46541751 drat 0.25168646 cyl 0.21803403 am 0.02139715 vs 0.00000000 gear 0.00000000 carb 0.00000000 == 7/7 Evaluate == AWE Evaluation RMSE: 1.8639 | MAE: 1.3358 | MAPE: 7.23% | R2: 0.9249 == 1/7 Explore == AWE Exploratory Summary Rows: 32 | Columns: 11 Numeric variables: 11 | Categorical variables: 0 No missing values detected. Numeric summary: mean sd min q25 median q75 max skewness kurtosis mpg 20.091 6.027 10.400 15.425 19.200 22.80 33.900 0.611 -0.373 cyl 6.188 1.786 4.000 4.000 6.000 8.00 8.000 -0.175 -1.762 disp 230.722 123.939 71.100 120.825 196.300 326.00 472.000 0.382 -1.207 hp 146.688 68.563 52.000 96.500 123.000 180.00 335.000 0.726 -0.136 drat 3.597 0.535 2.760 3.080 3.695 3.92 4.930 0.266 -0.715 wt 3.217 0.978 1.513 2.581 3.325 3.61 5.424 0.423 -0.023 qsec 17.849 1.787 14.500 16.892 17.710 18.90 22.900 0.369 0.335 vs 0.438 0.504 0.000 0.000 0.000 1.00 1.000 0.240 -2.002 am 0.406 0.499 0.000 0.000 0.000 1.00 1.000 0.364 -1.925 gear 3.688 0.738 3.000 3.000 4.000 4.00 5.000 0.529 -1.070 carb 2.812 1.615 1.000 2.000 2.000 4.00 8.000 1.051 1.257 n_outliers mpg 1 cyl 0 disp 0 hp 1 drat 0 wt 3 qsec 1 vs 0 am 0 gear 0 carb 1 == 2/7 Preprocess == Train: 24 rows | Test: 8 rows == 3/7 Select features == AWE Feature Selection Selected 5 predictor(s): cyl, wt, disp, hp, drat Univariate scores: variable score cyl 0.8374984 wt 0.8269096 disp 0.8146545 hp 0.7636051 drat 0.6063971 vs 0.6036454 carb 0.5730554 am 0.5358443 qsec 0.5034704 gear 0.3400519 == 4/7 Model == AWE Model Selection Target: mpg (continuous) Candidates tried: 15 Leaderboard: model metric mean_score sd_score ranger RMSE 2.778162 0.4767664 random_forest RMSE 2.926019 0.5347814 gradient_boosting RMSE 2.959793 0.7024620 mars RMSE 3.245521 0.3023847 ridge_regression RMSE 3.272374 0.3989269 elastic_net RMSE 3.438883 0.4787574 knn_regression RMSE 3.535897 0.3472192 xgboost RMSE 3.583508 0.5897396 svm_radial RMSE 3.585116 1.4714455 lasso_regression RMSE 3.603645 0.4675328 svm_linear RMSE 3.757762 0.5252028 linear_regression RMSE 3.833493 1.0791369 pls_regression RMSE 3.833493 1.0791369 robust_regression RMSE 4.167371 0.9747980 decision_tree RMSE 6.158014 1.1204867 Best model: ranger == 5/7 Wrap == AWE Wrapper Model: ranger Target: mpg (continuous) Predictors: 5 Class probabilities available: FALSE == 6/7 Explain == AWE Explainer Permutation importance (top variables): variable importance hp 1.6808376 disp 1.2197109 wt 1.0084173 cyl 0.7415920 drat 0.2113392 == 7/7 Evaluate == AWE Evaluation RMSE: 2.3008 | MAE: 1.5977 | MAPE: 8.49% | R2: 0.8856 [ FAIL 0 | WARN 133 | SKIP 0 | PASS 77 ] [ FAIL 0 | WARN 133 | SKIP 0 | PASS 77 ] > > proc.time() user system elapsed 101.56 4.00 105.12