Report for xgboost::xgb.train

Report for xgboost::xgb.train

Usage

report_xgboost(
  model,
  validation_data = NULL,
  label = NULL,
  file = "xgboost",
  w = 10,
  h = 10,
  base_size = 10,
  title = "",
  fast = FALSE
)

Arguments

model

object from xgboost::xgb.train

validation_data

validation data

label

outcome variable name

file

output filename

w

width of pdf file

h

height of pdf file

base_size

base font size

title

plot title

fast

if TRUE error values are not saved in output

Value

Invisibly, a list with

plots

ggplot objects: regression (observed vs predicted on the validation data), performance (cut-point diagnostics, not for regression objectives), depth, cover, weight (tree structure), error (training log) and importance.

result

tables: importance (from xgboost::xgb.importance), parameters (hyperparameters), model_call, evaluation_log, model_summary (objective, boosting rounds, number of features, and with early stopping the best iteration and score). With validation data, there is also validation_metrics (regression: N, RMSE, MAE, mean error, R squared; binary classification: N, AUC, log loss and accuracy at a 0.5 cut-off).

observed, predicted

validation outcome and predictions (NULL without validation_data).

feature_names

the model's features, in the model's order.

Details

Works with xgboost 3, which stores the call, parameters and evaluation log as attributes of the model, and with older versions that store them as list elements. Validation predictions use the model's own feature order. When file is given, the plots are written to <file>.pdf and the tables to <file>.xlsx (sheets: confusion matrix for classification, Model Summary, Validation Metrics, Feature Importance, Hyperparameters and, unless fast = TRUE, Evaluation Log).

Examples

infert_formula<-formula(case~education+spontaneous+induced)
boston_formula<-formula(medv~crim+zn+indus+chas+nox+rm+age+
                         dis+rad+tax+ptratio+black+lstat)
train_test_classification<-k_fold(df=infert,model_formula=infert_formula)
#> Fold Cases: 1 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 2 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 3 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 4 Train: 224 Test: 24 Total: 248 Unique Train: 224 Unique Test: 24 
#> Fold Cases: 5 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 6 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 7 Train: 224 Test: 24 Total: 248 Unique Train: 224 Unique Test: 24 
#> Fold Cases: 8 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 9 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
#> Fold Cases: 10 Train: 223 Test: 25 Total: 248 Unique Train: 223 Unique Test: 25 
train_test_regression<-k_fold(df=MASS::Boston,model_formula=boston_formula)
#> Fold Cases: 1 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
#> Fold Cases: 2 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
#> Fold Cases: 3 Train: 456 Test: 50 Total: 506 Unique Train: 456 Unique Test: 50 
#> Fold Cases: 4 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
#> Fold Cases: 5 Train: 456 Test: 50 Total: 506 Unique Train: 456 Unique Test: 50 
#> Fold Cases: 6 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
#> Fold Cases: 7 Train: 456 Test: 50 Total: 506 Unique Train: 456 Unique Test: 50 
#> Fold Cases: 8 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
#> Fold Cases: 9 Train: 456 Test: 50 Total: 506 Unique Train: 456 Unique Test: 50 
#> Fold Cases: 10 Train: 455 Test: 51 Total: 506 Unique Train: 455 Unique Test: 51 
xgb_classification<-xgboost::xgb.train(
                    params=xgboost::xgb.params(objective="binary:logistic"),
                    data=train_test_classification$xgb$f1$train,
                    evals=train_test_classification$xgb$f1$watchlist,
                    nround=20)
#> [1]	train-logloss:0.601567	test-logloss:0.535093 
#> [2]	train-logloss:0.575618	test-logloss:0.521200 
#> [3]	train-logloss:0.559581	test-logloss:0.515340 
#> [4]	train-logloss:0.549645	test-logloss:0.511429 
#> [5]	train-logloss:0.543232	test-logloss:0.510642 
#> [6]	train-logloss:0.539043	test-logloss:0.509685 
#> [7]	train-logloss:0.536289	test-logloss:0.509001 
#> [8]	train-logloss:0.534253	test-logloss:0.509510 
#> [9]	train-logloss:0.532991	test-logloss:0.508879 
#> [10]	train-logloss:0.531934	test-logloss:0.509436 
#> [11]	train-logloss:0.531048	test-logloss:0.513843 
#> [12]	train-logloss:0.530487	test-logloss:0.518176 
#> [13]	train-logloss:0.530105	test-logloss:0.521507 
#> [14]	train-logloss:0.529684	test-logloss:0.517731 
#> [15]	train-logloss:0.529402	test-logloss:0.520672 
#> [16]	train-logloss:0.529249	test-logloss:0.522455 
#> [17]	train-logloss:0.528879	test-logloss:0.518311 
#> [18]	train-logloss:0.528678	test-logloss:0.520694 
#> [19]	train-logloss:0.528140	test-logloss:0.515638 
#> [20]	train-logloss:0.527980	test-logloss:0.518252 
xgb_regression<-xgboost::xgb.train(
                data=train_test_regression$xgb$f1$train,
                evals=train_test_regression$xgb$f1$watchlist,
                nround=20)
#> [1]	train-rmse:6.877490	test-rmse:6.881132 
#> [2]	train-rmse:5.207159	test-rmse:5.844378 
#> [3]	train-rmse:4.026195	test-rmse:5.177089 
#> [4]	train-rmse:3.169878	test-rmse:4.787926 
#> [5]	train-rmse:2.581531	test-rmse:4.580796 
#> [6]	train-rmse:2.100192	test-rmse:4.304045 
#> [7]	train-rmse:1.777937	test-rmse:4.165161 
#> [8]	train-rmse:1.534497	test-rmse:3.980251 
#> [9]	train-rmse:1.379046	test-rmse:3.961146 
#> [10]	train-rmse:1.247219	test-rmse:3.875961 
#> [11]	train-rmse:1.135899	test-rmse:3.798032 
#> [12]	train-rmse:1.050888	test-rmse:3.749944 
#> [13]	train-rmse:0.959931	test-rmse:3.728645 
#> [14]	train-rmse:0.905722	test-rmse:3.721090 
#> [15]	train-rmse:0.853247	test-rmse:3.731942 
#> [16]	train-rmse:0.822476	test-rmse:3.729514 
#> [17]	train-rmse:0.772964	test-rmse:3.678861 
#> [18]	train-rmse:0.731270	test-rmse:3.686072 
#> [19]	train-rmse:0.691505	test-rmse:3.695998 
#> [20]	train-rmse:0.676991	test-rmse:3.684742 
report_xgboost(model=xgb_classification,
               validation_data=train_test_classification$f$test$f1,
               label=train_test_classification$outcome,
               file="Classification")
#> $observed_predicted
#> 
#> $plot_performance
#> 
#> $cut_performance
#>   cut_point Overall Collumn_Observed.1 Collumn_Observed.2 Row_Predicted.1 Row_Predicted.2 Mean_proportion
#> 1   0.07838    0.40               1.00               0.25            0.25             1.0          0.6250
#> 2   0.17838    0.68               0.88               0.33            0.70             0.6          0.6275
#> 3   0.27838    0.68               0.83               0.29            0.75             0.4          0.5675
#> 4   0.37838    0.60               0.75               0.00            0.75             0.0          0.3750
#> 5   0.47838    0.60               0.75               0.00            0.75             0.0          0.3750
#> 
#> $cut
#> [1] 0.1784
#> 
#> $confusion_matrix
#>         0    1   sum    p
#> 0   14.00 2.00 16.00 0.88
#> 1    6.00 3.00  9.00 0.33
#> sum 20.00 5.00 25.00 1.00
#> p    0.70 0.60  1.00 0.68
#> 
#> $observed_predicted

#> 
#> $plot_performance

#> 
#> $cut_performance
#>   cut_point Overall Collumn_Observed.1 Collumn_Observed.2 Row_Predicted.1 Row_Predicted.2 Mean_proportion
#> 1   0.07838    0.40               1.00               0.25            0.25             1.0          0.6250
#> 2   0.17838    0.68               0.88               0.33            0.70             0.6          0.6275
#> 3   0.27838    0.68               0.83               0.29            0.75             0.4          0.5675
#> 4   0.37838    0.60               0.75               0.00            0.75             0.0          0.3750
#> 5   0.47838    0.60               0.75               0.00            0.75             0.0          0.3750
#> 
#> $cut
#> [1] 0.1784
#> 
#> $confusion_matrix
#>         0    1   sum    p
#> 0   14.00 2.00 16.00 0.88
#> 1    6.00 3.00  9.00 0.33
#> sum 20.00 5.00 25.00 1.00
#> p    0.70 0.60  1.00 0.68
#> 





report_xgboost(model=xgb_regression,
               validation_data=train_test_regression$f$test$f1,
               label=train_test_regression$outcome,
               file="Regression")
#> $observed_predicted
#> 
#> $observed_predicted

#>