Goal is to automate building and tuning a classification model to predict employee attrition, using the h2o::h2o.automl.
Import the cleaned data from Module 7.
library(h2o)##
## ----------------------------------------------------------------------
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## Your next step is to start H2O:
## > h2o.init()
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## For H2O package documentation, ask for help:
## > ??h2o
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## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
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## • Learn how to get started at https://www.tidymodels.org/start/library(tidyquant)## Loading required package: PerformanceAnalytics
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## method from
## as.zoo.data.frame zoodata <- read_csv("../00_data/data_wrangled/data_clean.csv") %>%
# h2o requires all variables to be either numeric or factors
mutate(across(where(is.character), factor))## Rows: 1470 Columns: 32
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (8): Attrition, BusinessTravel, Department, EducationField, Gender, Job...
## dbl (24): Age, DailyRate, DistanceFromHome, Education, EmployeeNumber, Envir...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.set.seed(1234)
data_split <- initial_split(data, strata = "Attrition")
train_tbl <- training(data_split)
test_tbl <- testing(data_split)recipe_obj <- recipe(Attrition ~ ., data = train_tbl) %>%
# Remove zero variance variables
step_zv(all_predictors()) # Initialize h2o
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 49 minutes 23 seconds
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 4 months and 18 days
## H2O cluster name: H2O_started_from_R_maddie_qns693
## H2O cluster total nodes: 1
## H2O cluster total memory: 1.68 GB
## H2O cluster total cores: 4
## H2O cluster allowed cores: 4
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.3.3 (2024-02-29)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (4 months and 18 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.htmlsplit.h2o <- h2o.splitFrame(as.h2o(train_tbl), ratios = c(0.85), seed = 2345)## | | | 0% | |======================================================================| 100%train_h2o <- split.h2o[[1]]
valid_h2o <- split.h2o[[2]]
test_h2o <- as.h2o(test_tbl)## | | | 0% | |======================================================================| 100%y <- "Attrition"
x <- setdiff(names(train_tbl), y)
models_h2o <- h2o.automl(
x = x,
y = y,
training_frame = train_h2o,
validation_frame = valid_h2o,
leaderboard_frame = test_h2o,
# max_runtime_secs = 30,
max_models = 10,
exclude_algos = "DeepLearning",
nfolds = 5,
seed = 3456
)## | | | 0%
## 12:08:10.450: User specified a validation frame with cross-validation still enabled. Please note that the models will still be validated using cross-validation only, the validation frame will be used to provide purely informative validation metrics on the trained models. | |==== | 5% | |======= | 10% | |=========== | 15% | |============== | 21% | |==================== | 28% | |======================================================================| 100%models_h2o %>% typeof()## [1] "S4"models_h2o %>% slotNames()## [1] "project_name" "leader" "leaderboard" "event_log"
## [5] "modeling_steps" "training_info"models_h2o@leaderboard## model_id auc logloss
## 1 GBM_1_AutoML_5_20240509_120810 0.8552319 0.3243584
## 2 GLM_1_AutoML_5_20240509_120810 0.8400216 0.3186222
## 3 StackedEnsemble_BestOfFamily_1_AutoML_5_20240509_120810 0.8386731 0.3679170
## 4 StackedEnsemble_AllModels_1_AutoML_5_20240509_120810 0.8330097 0.3360015
## 5 XGBoost_1_AutoML_5_20240509_120810 0.8303128 0.3350992
## 6 GBM_2_AutoML_5_20240509_120810 0.8120820 0.3397394
## aucpr mean_per_class_error rmse mse
## 1 0.6161812 0.2221683 0.3118296 0.09723772
## 2 0.6515822 0.2369741 0.3064601 0.09391777
## 3 0.6224065 0.2087379 0.3035404 0.09213679
## 4 0.6167542 0.2087379 0.3044454 0.09268702
## 5 0.5727296 0.2367314 0.3163680 0.10008873
## 6 0.5663229 0.2165858 0.3174311 0.10076251
##
## [12 rows x 7 columns]models_h2o@leader## Model Details:
## ==============
##
## H2OBinomialModel: gbm
## Model ID: GBM_1_AutoML_5_20240509_120810
## Model Summary:
## number_of_trees number_of_internal_trees model_size_in_bytes min_depth
## 1 59 59 7762 3
## max_depth mean_depth min_leaves max_leaves mean_leaves
## 1 5 3.93220 5 7 5.81356
##
##
## H2OBinomialMetrics: gbm
## ** Reported on training data. **
##
## MSE: 0.07760747
## RMSE: 0.2785812
## LogLoss: 0.2692952
## Mean Per-Class Error: 0.1906261
## AUC: 0.9218063
## AUCPR: 0.795637
## Gini: 0.8436125
## R^2: 0.4223142
##
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
## No Yes Error Rate
## No 766 22 0.027919 =22/788
## Yes 53 97 0.353333 =53/150
## Totals 819 119 0.079957 =75/938
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.333741 0.721190 97
## 2 max f2 0.189332 0.756155 179
## 3 max f0point5 0.439043 0.781938 63
## 4 max accuracy 0.346929 0.920043 91
## 5 max precision 0.852824 1.000000 0
## 6 max recall 0.039666 1.000000 359
## 7 max specificity 0.852824 1.000000 0
## 8 max absolute_mcc 0.333741 0.681408 97
## 9 max min_per_class_accuracy 0.193072 0.846447 176
## 10 max mean_per_class_accuracy 0.221420 0.851937 152
## 11 max tns 0.852824 788.000000 0
## 12 max fns 0.852824 149.000000 0
## 13 max fps 0.007684 788.000000 399
## 14 max tps 0.039666 150.000000 359
## 15 max tnr 0.852824 1.000000 0
## 16 max fnr 0.852824 0.993333 0
## 17 max fpr 0.007684 1.000000 399
## 18 max tpr 0.039666 1.000000 359
##
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
## H2OBinomialMetrics: gbm
## ** Reported on validation data. **
## ** Validation metrics **
##
## MSE: 0.1054871
## RMSE: 0.3247877
## LogLoss: 0.3436335
## Mean Per-Class Error: 0.2551743
## AUC: 0.8295207
## AUCPR: 0.5567645
## Gini: 0.6590414
## R^2: 0.2367413
##
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
## No Yes Error Rate
## No 122 14 0.102941 =14/136
## Yes 11 16 0.407407 =11/27
## Totals 133 30 0.153374 =25/163
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.309375 0.561404 29
## 2 max f2 0.126346 0.666667 71
## 3 max f0point5 0.342225 0.569106 23
## 4 max accuracy 0.541568 0.865031 4
## 5 max precision 0.842469 1.000000 0
## 6 max recall 0.035645 1.000000 142
## 7 max specificity 0.842469 1.000000 0
## 8 max absolute_mcc 0.309375 0.469734 29
## 9 max min_per_class_accuracy 0.219496 0.703704 44
## 10 max mean_per_class_accuracy 0.126346 0.767974 71
## 11 max tns 0.842469 136.000000 0
## 12 max fns 0.842469 26.000000 0
## 13 max fps 0.009418 136.000000 162
## 14 max tps 0.035645 27.000000 142
## 15 max tnr 0.842469 1.000000 0
## 16 max fnr 0.842469 0.962963 0
## 17 max fpr 0.009418 1.000000 162
## 18 max tpr 0.035645 1.000000 142
##
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
## H2OBinomialMetrics: gbm
## ** Reported on cross-validation data. **
## ** 5-fold cross-validation on training data (Metrics computed for combined holdout predictions) **
##
## MSE: 0.1021651
## RMSE: 0.3196328
## LogLoss: 0.3464857
## Mean Per-Class Error: 0.2604484
## AUC: 0.8034052
## AUCPR: 0.5856122
## Gini: 0.6068105
## R^2: 0.2395148
##
## Confusion Matrix (vertical: actual; across: predicted) for F1-optimal threshold:
## No Yes Error Rate
## No 719 69 0.087563 =69/788
## Yes 65 85 0.433333 =65/150
## Totals 784 154 0.142857 =134/938
##
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.274891 0.559211 116
## 2 max f2 0.162481 0.604925 211
## 3 max f0point5 0.351807 0.622605 74
## 4 max accuracy 0.405894 0.879531 50
## 5 max precision 0.846770 1.000000 0
## 6 max recall 0.012518 1.000000 395
## 7 max specificity 0.846770 1.000000 0
## 8 max absolute_mcc 0.351807 0.487870 74
## 9 max min_per_class_accuracy 0.167255 0.733503 206
## 10 max mean_per_class_accuracy 0.197244 0.741015 177
## 11 max tns 0.846770 788.000000 0
## 12 max fns 0.846770 149.000000 0
## 13 max fps 0.005852 788.000000 399
## 14 max tps 0.012518 150.000000 395
## 15 max tnr 0.846770 1.000000 0
## 16 max fnr 0.846770 0.993333 0
## 17 max fpr 0.005852 1.000000 399
## 18 max tpr 0.012518 1.000000 395
##
## Gains/Lift Table: Extract with `h2o.gainsLift(<model>, <data>)` or `h2o.gainsLift(<model>, valid=<T/F>, xval=<T/F>)`
## Cross-Validation Metrics Summary:
## mean sd cv_1_valid cv_2_valid cv_3_valid
## accuracy 0.874206 0.015736 0.893617 0.867021 0.856383
## auc 0.800818 0.046494 0.841983 0.763080 0.753165
## err 0.125794 0.015736 0.106383 0.132979 0.143617
## err_count 23.600000 2.966479 20.000000 25.000000 27.000000
## f0point5 0.607235 0.055991 0.669014 0.576923 0.538462
## f1 0.583766 0.061648 0.655172 0.545455 0.509091
## f2 0.567463 0.093249 0.641892 0.517241 0.482759
## lift_top_group 6.253334 0.018257 6.266667 6.266667 6.266667
## logloss 0.347126 0.028586 0.322462 0.375521 0.372290
## max_per_class_error 0.440000 0.118790 0.366667 0.500000 0.533333
## mcc 0.516979 0.067355 0.592861 0.470996 0.428220
## mean_per_class_accuracy 0.747000 0.050090 0.788186 0.718354 0.698523
## mean_per_class_error 0.253000 0.050090 0.211814 0.281646 0.301477
## mse 0.102380 0.009717 0.092129 0.112453 0.110710
## pr_auc 0.582737 0.089843 0.704703 0.500874 0.490292
## precision 0.627903 0.077290 0.678571 0.600000 0.560000
## r2 0.237864 0.073080 0.313040 0.161491 0.174484
## recall 0.560000 0.118790 0.633333 0.500000 0.466667
## rmse 0.319678 0.015247 0.303527 0.335340 0.332732
## specificity 0.934000 0.027641 0.943038 0.936709 0.930380
## cv_4_valid cv_5_valid
## accuracy 0.887701 0.866310
## auc 0.789172 0.856688
## err 0.112299 0.133690
## err_count 21.000000 25.000000
## f0point5 0.660377 0.591398
## f1 0.571429 0.637681
## f2 0.503597 0.691824
## lift_top_group 6.233333 6.233333
## logloss 0.352718 0.312637
## max_per_class_error 0.533333 0.266667
## mcc 0.528187 0.564630
## mean_per_class_accuracy 0.717410 0.812527
## mean_per_class_error 0.282590 0.187473
## mse 0.104156 0.092451
## pr_auc 0.588982 0.628833
## precision 0.736842 0.564103
## r2 0.226700 0.313605
## recall 0.466667 0.733333
## rmse 0.322733 0.304058
## specificity 0.968153 0.891720?h2o.getModel
?h2o.saveModel
?h2o.loadModel
# h2o.getModel("GBM_1_AutoML_4_20240509_120305") %>%
#h2o.saveModel("h20_models/")
best_model <- h2o.loadModel("h20_models/GBM_1_AutoML_4_20240509_120305")predictions <- h2o.predict(best_model, newdata = test_h2o)## | | | 0% | |======================================================================| 100%predictions_tbl <- predictions %>%
as_tibble()
predictions_tbl %>%
bind_cols(test_tbl)## # A tibble: 369 × 35
## predict No Yes Age Attrition BusinessTravel DailyRate Department
## <fct> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <fct>
## 1 Yes 0.649 0.351 59 No Travel_Rarely 1324 Research & …
## 2 No 0.813 0.187 35 No Travel_Rarely 809 Research & …
## 3 No 0.939 0.0608 34 No Travel_Rarely 1346 Research & …
## 4 Yes 0.589 0.411 22 No Non-Travel 1123 Research & …
## 5 No 0.920 0.0803 53 No Travel_Rarely 1219 Sales
## 6 No 0.945 0.0550 24 No Non-Travel 673 Research & …
## 7 No 0.757 0.243 21 No Travel_Rarely 391 Research & …
## 8 No 0.953 0.0471 34 Yes Travel_Rarely 699 Research & …
## 9 No 0.981 0.0187 53 No Travel_Rarely 1282 Research & …
## 10 Yes 0.499 0.501 32 Yes Travel_Frequently 1125 Research & …
## # ℹ 359 more rows
## # ℹ 27 more variables: DistanceFromHome <dbl>, Education <dbl>,
## # EducationField <fct>, EmployeeNumber <dbl>, EnvironmentSatisfaction <dbl>,
## # Gender <fct>, HourlyRate <dbl>, JobInvolvement <dbl>, JobLevel <dbl>,
## # JobRole <fct>, JobSatisfaction <dbl>, MaritalStatus <fct>,
## # MonthlyIncome <dbl>, MonthlyRate <dbl>, NumCompaniesWorked <dbl>,
## # OverTime <fct>, PercentSalaryHike <dbl>, PerformanceRating <dbl>, …?h2o.performance
performance_h2o <- h2o.performance(best_model, newdata = test_h2o)
typeof(performance_h2o)## [1] "S4"slotNames(performance_h2o) ## [1] "algorithm" "on_train" "on_valid" "on_xval" "metrics"performance_h2o@metrics## $model
## $model$`__meta`
## $model$`__meta`$schema_version
## [1] 3
##
## $model$`__meta`$schema_name
## [1] "ModelKeyV3"
##
## $model$`__meta`$schema_type
## [1] "Key<Model>"
##
##
## $model$name
## [1] "GBM_1_AutoML_4_20240509_120305"
##
## $model$type
## [1] "Key<Model>"
##
## $model$URL
## [1] "/3/Models/GBM_1_AutoML_4_20240509_120305"
##
##
## $model_checksum
## [1] "6097428537100003408"
##
## $frame
## $frame$name
## [1] "test_tbl_sid_b418_3"
##
##
## $frame_checksum
## [1] "-54413681510283746"
##
## $description
## NULL
##
## $scoring_time
## [1] 1.715271e+12
##
## $predictions
## NULL
##
## $MSE
## [1] 0.09723772
##
## $RMSE
## [1] 0.3118296
##
## $nobs
## [1] 369
##
## $custom_metric_name
## NULL
##
## $custom_metric_value
## [1] 0
##
## $r2
## [1] 0.2858692
##
## $logloss
## [1] 0.3243584
##
## $AUC
## [1] 0.8552319
##
## $pr_auc
## [1] 0.6161812
##
## $Gini
## [1] 0.7104639
##
## $mean_per_class_error
## [1] 0.2221683
##
## $domain
## [1] "No" "Yes"
##
## $cm
## $cm$`__meta`
## $cm$`__meta`$schema_version
## [1] 3
##
## $cm$`__meta`$schema_name
## [1] "ConfusionMatrixV3"
##
## $cm$`__meta`$schema_type
## [1] "ConfusionMatrix"
##
##
## $cm$table
## Confusion Matrix: Row labels: Actual class; Column labels: Predicted class
## No Yes Error Rate
## No 285 24 0.0777 = 24 / 309
## Yes 22 38 0.3667 = 22 / 60
## Totals 307 62 0.1247 = 46 / 369
##
##
## $thresholds_and_metric_scores
## Metrics for Thresholds: Binomial metrics as a function of classification thresholds
## threshold f1 f2 f0point5 accuracy precision recall specificity
## 1 0.833189 0.032787 0.020747 0.078125 0.840108 1.000000 0.016667 1.000000
## 2 0.815966 0.064516 0.041322 0.147059 0.842818 1.000000 0.033333 1.000000
## 3 0.773643 0.095238 0.061728 0.208333 0.845528 1.000000 0.050000 1.000000
## 4 0.769062 0.093750 0.061475 0.197368 0.842818 0.750000 0.050000 0.996764
## 5 0.699918 0.092308 0.061224 0.187500 0.840108 0.600000 0.050000 0.993528
## absolute_mcc min_per_class_accuracy mean_per_class_accuracy tns fns fps tps
## 1 0.118299 0.016667 0.508333 309 59 0 1
## 2 0.167527 0.033333 0.516667 309 58 0 2
## 3 0.205458 0.050000 0.525000 309 57 0 3
## 4 0.166643 0.050000 0.523382 308 57 1 3
## 5 0.138926 0.050000 0.521764 307 57 2 3
## tnr fnr fpr tpr idx
## 1 1.000000 0.983333 0.000000 0.016667 0
## 2 1.000000 0.966667 0.000000 0.033333 1
## 3 1.000000 0.950000 0.000000 0.050000 2
## 4 0.996764 0.950000 0.003236 0.050000 3
## 5 0.993528 0.950000 0.006472 0.050000 4
##
## ---
## threshold f1 f2 f0point5 accuracy precision recall
## 364 0.014260 0.283019 0.496689 0.197889 0.176152 0.164835 1.000000
## 365 0.013808 0.282353 0.495868 0.197368 0.173442 0.164384 1.000000
## 366 0.010435 0.281690 0.495050 0.196850 0.170732 0.163934 1.000000
## 367 0.010104 0.281030 0.494234 0.196335 0.168022 0.163488 1.000000
## 368 0.009857 0.280374 0.493421 0.195822 0.165312 0.163043 1.000000
## 369 0.007542 0.279720 0.492611 0.195313 0.162602 0.162602 1.000000
## specificity absolute_mcc min_per_class_accuracy mean_per_class_accuracy tns
## 364 0.016181 0.051645 0.016181 0.508091 5
## 365 0.012945 0.046130 0.012945 0.506472 4
## 366 0.009709 0.039895 0.009709 0.504854 3
## 367 0.006472 0.032530 0.006472 0.503236 2
## 368 0.003236 0.022971 0.003236 0.501618 1
## 369 0.000000 0.000000 0.000000 0.500000 0
## fns fps tps tnr fnr fpr tpr idx
## 364 0 304 60 0.016181 0.000000 0.983819 1.000000 363
## 365 0 305 60 0.012945 0.000000 0.987055 1.000000 364
## 366 0 306 60 0.009709 0.000000 0.990291 1.000000 365
## 367 0 307 60 0.006472 0.000000 0.993528 1.000000 366
## 368 0 308 60 0.003236 0.000000 0.996764 1.000000 367
## 369 0 309 60 0.000000 0.000000 1.000000 1.000000 368
##
## $max_criteria_and_metric_scores
## Maximum Metrics: Maximum metrics at their respective thresholds
## metric threshold value idx
## 1 max f1 0.271275 0.622951 61
## 2 max f2 0.174971 0.692521 120
## 3 max f0point5 0.413488 0.640244 25
## 4 max accuracy 0.413488 0.880759 25
## 5 max precision 0.833189 1.000000 0
## 6 max recall 0.016687 1.000000 359
## 7 max specificity 0.833189 1.000000 0
## 8 max absolute_mcc 0.279919 0.550511 55
## 9 max min_per_class_accuracy 0.188312 0.796117 110
## 10 max mean_per_class_accuracy 0.201228 0.802670 101
## 11 max tns 0.833189 309.000000 0
## 12 max fns 0.833189 59.000000 0
## 13 max fps 0.007542 309.000000 368
## 14 max tps 0.016687 60.000000 359
## 15 max tnr 0.833189 1.000000 0
## 16 max fnr 0.833189 0.983333 0
## 17 max fpr 0.007542 1.000000 368
## 18 max tpr 0.016687 1.000000 359
##
## $gains_lift_table
## Gains/Lift Table: Avg response rate: 16.26 %, avg score: 15.94 %
## group cumulative_data_fraction lower_threshold lift cumulative_lift
## 1 1 0.01084011 0.722044 4.612500 4.612500
## 2 2 0.02168022 0.604916 4.612500 4.612500
## 3 3 0.03252033 0.553996 6.150000 5.125000
## 4 4 0.04065041 0.504840 6.150000 5.330000
## 5 5 0.05149051 0.493006 4.612500 5.178947
## 6 6 0.10027100 0.369856 3.416667 4.321622
## 7 7 0.15176152 0.279524 3.236842 3.953571
## 8 8 0.20054201 0.242804 1.366667 3.324324
## 9 9 0.30081301 0.187608 1.329730 2.659459
## 10 10 0.40108401 0.138477 0.332432 2.077703
## 11 11 0.50135501 0.112372 0.498649 1.761892
## 12 12 0.59891599 0.084050 0.170833 1.502715
## 13 13 0.69918699 0.061169 0.498649 1.358721
## 14 14 0.79945799 0.043965 0.332432 1.230000
## 15 15 0.89972900 0.030880 0.000000 1.092922
## 16 16 1.00000000 0.007542 0.166216 1.000000
## response_rate score cumulative_response_rate cumulative_score
## 1 0.750000 0.797965 0.750000 0.797965
## 2 0.750000 0.665700 0.750000 0.731832
## 3 1.000000 0.575337 0.833333 0.679667
## 4 1.000000 0.516722 0.866667 0.647078
## 5 0.750000 0.500101 0.842105 0.616136
## 6 0.555556 0.412515 0.702703 0.517077
## 7 0.526316 0.317841 0.642857 0.449479
## 8 0.222222 0.262525 0.540541 0.404004
## 9 0.216216 0.214250 0.432432 0.340752
## 10 0.054054 0.163306 0.337838 0.296391
## 11 0.081081 0.124491 0.286486 0.262011
## 12 0.027778 0.095949 0.244344 0.234960
## 13 0.081081 0.073852 0.220930 0.211855
## 14 0.054054 0.053558 0.200000 0.192001
## 15 0.000000 0.037644 0.177711 0.174799
## 16 0.027027 0.020760 0.162602 0.159353
## capture_rate cumulative_capture_rate gain cumulative_gain
## 1 0.050000 0.050000 361.250000 361.250000
## 2 0.050000 0.100000 361.250000 361.250000
## 3 0.066667 0.166667 515.000000 412.500000
## 4 0.050000 0.216667 515.000000 433.000000
## 5 0.050000 0.266667 361.250000 417.894737
## 6 0.166667 0.433333 241.666667 332.162162
## 7 0.166667 0.600000 223.684211 295.357143
## 8 0.066667 0.666667 36.666667 232.432432
## 9 0.133333 0.800000 32.972973 165.945946
## 10 0.033333 0.833333 -66.756757 107.770270
## 11 0.050000 0.883333 -50.135135 76.189189
## 12 0.016667 0.900000 -82.916667 50.271493
## 13 0.050000 0.950000 -50.135135 35.872093
## 14 0.033333 0.983333 -66.756757 23.000000
## 15 0.000000 0.983333 -100.000000 9.292169
## 16 0.016667 1.000000 -83.378378 0.000000
## kolmogorov_smirnov
## 1 0.046764
## 2 0.093528
## 3 0.160194
## 4 0.210194
## 5 0.256958
## 6 0.397735
## 7 0.535275
## 8 0.556634
## 9 0.596117
## 10 0.516181
## 11 0.456149
## 12 0.359547
## 13 0.299515
## 14 0.219579
## 15 0.099838
## 16 0.000000h2o.auc(performance_h2o)## [1] 0.8552319h2o.confusionMatrix(performance_h2o)## Confusion Matrix (vertical: actual; across: predicted) for max f1 @ threshold = 0.271274787838988:
## No Yes Error Rate
## No 285 24 0.077670 =24/309
## Yes 22 38 0.366667 =22/60
## Totals 307 62 0.124661 =46/369h2o.metric(performance_h2o) %>% as_tibble() %>% filter(threshold %>% between (0.27, 0.28))## # A tibble: 7 × 20
## threshold f1 f2 f0point5 accuracy precision recall specificity
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0.280 0.621 0.608 0.634 0.881 0.643 0.6 0.935
## 2 0.278 0.615 0.606 0.625 0.878 0.632 0.6 0.932
## 3 0.275 0.610 0.604 0.616 0.875 0.621 0.6 0.929
## 4 0.274 0.605 0.602 0.608 0.873 0.610 0.6 0.926
## 5 0.273 0.617 0.617 0.617 0.875 0.617 0.617 0.926
## 6 0.272 0.612 0.615 0.609 0.873 0.607 0.617 0.922
## 7 0.271 0.623 0.629 0.617 0.875 0.613 0.633 0.922
## # ℹ 12 more variables: absolute_mcc <dbl>, min_per_class_accuracy <dbl>,
## # mean_per_class_accuracy <dbl>, tns <dbl>, fns <dbl>, fps <dbl>, tps <dbl>,
## # tnr <dbl>, fnr <dbl>, fpr <dbl>, tpr <dbl>, idx <int>