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)## Warning: package 'h2o' was built under R version 4.3.3##
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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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## 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()) h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 1 hours 49 minutes
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 10 months and 30 days
## H2O cluster name: H2O_started_from_R_eliza_fhp551
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.53 GB
## H2O cluster total cores: 16
## H2O cluster allowed cores: 16
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.3.2 (2023-10-31 ucrt)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (10 months and 30 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)##
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|======================================================================| 100%train_h2o <- split.h2o[[1]]
valid_h2o <- split.h2o[[2]]
test_h2o <- as.h2o(test_tbl)##
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|======================================================================| 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
)##
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|========= | 12%
## 23:32:17.715: 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.
## 23:32:17.720: AutoML: XGBoost is not available; skipping it.
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|============================================ | 62%
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|======================================================================| 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_grid_1_AutoML_5_20241119_233217_model_1 0.8589536 0.3269150
## 2 StackedEnsemble_BestOfFamily_1_AutoML_5_20241119_233217 0.8489213 0.3120121
## 3 GBM_1_AutoML_5_20241119_233217 0.8485976 0.3241789
## 4 StackedEnsemble_AllModels_1_AutoML_5_20241119_233217 0.8459547 0.3086959
## 5 GLM_1_AutoML_5_20241119_233217 0.8400216 0.3186222
## 6 GBM_4_AutoML_5_20241119_233217 0.8123517 0.3439750
## aucpr mean_per_class_error rmse mse
## 1 0.6193157 0.2031553 0.3141580 0.09869523
## 2 0.6388749 0.2508900 0.3021263 0.09128029
## 3 0.6066085 0.2404531 0.3118557 0.09725395
## 4 0.6272661 0.2393204 0.3000865 0.09005189
## 5 0.6515822 0.2369741 0.3064601 0.09391777
## 6 0.5489556 0.2654531 0.3191061 0.10182871
##
## [12 rows x 7 columns]#h2o.getModel("StackedEnsemble_BestOfFamily_4_AutoML_3_20241119_223041") %>%
#h2o.saveModel("C:/Users/eliza/Desktop/PSU_DAT3100/11_module13/h2o_models/")
best_model <- h2o.loadModel("h2o_models/StackedEnsemble_BestOfFamily_4_AutoML_3_20241119_223041")predictions <- h2o.predict(best_model, newdata = test_h2o)##
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|======================================================================| 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 No 0.752 0.248 59 No Travel_Rarely 1324 Research &…
## 2 No 0.907 0.0931 35 No Travel_Rarely 809 Research &…
## 3 No 0.869 0.131 34 No Travel_Rarely 1346 Research &…
## 4 Yes 0.589 0.411 22 No Non-Travel 1123 Research &…
## 5 No 0.985 0.0149 53 No Travel_Rarely 1219 Sales
## 6 No 0.973 0.0271 24 No Non-Travel 673 Research &…
## 7 No 0.813 0.187 21 No Travel_Rarely 391 Research &…
## 8 No 0.865 0.135 34 Yes Travel_Rarely 699 Research &…
## 9 No 0.998 0.00189 53 No Travel_Rarely 1282 Research &…
## 10 Yes 0.236 0.764 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>, …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] "StackedEnsemble_BestOfFamily_4_AutoML_3_20241119_223041"
##
## $model$type
## [1] "Key<Model>"
##
## $model$URL
## [1] "/3/Models/StackedEnsemble_BestOfFamily_4_AutoML_3_20241119_223041"
##
##
## $model_checksum
## [1] "4645873825974922480"
##
## $frame
## $frame$name
## [1] "test_tbl_sid_b501_3"
##
##
## $frame_checksum
## [1] "-54413681510283746"
##
## $description
## NULL
##
## $scoring_time
## [1] 1.732077e+12
##
## $predictions
## NULL
##
## $MSE
## [1] 0.0950839
##
## $RMSE
## [1] 0.3083568
##
## $nobs
## [1] 369
##
## $custom_metric_name
## NULL
##
## $custom_metric_value
## [1] 0
##
## $r2
## [1] 0.3016872
##
## $logloss
## [1] 0.3230807
##
## $AUC
## [1] 0.8370011
##
## $pr_auc
## [1] 0.6218335
##
## $Gini
## [1] 0.6740022
##
## $mean_per_class_error
## [1] 0.2251618
##
## $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 278 31 0.1003 = 31 / 309
## Yes 21 39 0.3500 = 21 / 60
## Totals 299 70 0.1409 = 52 / 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 0.944826 0.032787 0.020747 0.078125 0.840108 1.000000 0.016667 1.000000
## 2 0.915797 0.064516 0.041322 0.147059 0.842818 1.000000 0.033333 1.000000
## 3 0.884152 0.095238 0.061728 0.208333 0.845528 1.000000 0.050000 1.000000
## 4 0.833643 0.125000 0.081967 0.263158 0.848238 1.000000 0.066667 1.000000
## 5 0.831328 0.153846 0.102041 0.312500 0.850949 1.000000 0.083333 1.000000
## 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 0.237568 0.066667 0.533333 309 56 0 4
## 5 0.265973 0.083333 0.541667 309 55 0 5
## 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 1.000000 0.933333 0.000000 0.066667 3
## 5 1.000000 0.916667 0.000000 0.083333 4
##
## ---
## threshold f1 f2 f0point5 accuracy precision recall
## 364 0.002934 0.283019 0.496689 0.197889 0.176152 0.164835 1.000000
## 365 0.002767 0.282353 0.495868 0.197368 0.173442 0.164384 1.000000
## 366 0.002288 0.281690 0.495050 0.196850 0.170732 0.163934 1.000000
## 367 0.001886 0.281030 0.494234 0.196335 0.168022 0.163488 1.000000
## 368 0.001804 0.280374 0.493421 0.195822 0.165312 0.163043 1.000000
## 369 0.001307 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 0.327281 0.600000 69
## 2 max f2 0.209127 0.646067 115
## 3 max f0point5 0.532289 0.679348 30
## 4 max accuracy 0.532289 0.888889 30
## 5 max precision 0.944826 1.000000 0
## 6 max recall 0.012361 1.000000 333
## 7 max specificity 0.944826 1.000000 0
## 8 max absolute_mcc 0.532289 0.528419 30
## 9 max min_per_class_accuracy 0.209127 0.766667 115
## 10 max mean_per_class_accuracy 0.327281 0.774838 69
## 11 max tns 0.944826 309.000000 0
## 12 max fns 0.944826 59.000000 0
## 13 max fps 0.001307 309.000000 368
## 14 max tps 0.012361 60.000000 333
## 15 max tnr 0.944826 1.000000 0
## 16 max fnr 0.944826 0.983333 0
## 17 max fpr 0.001307 1.000000 368
## 18 max tpr 0.012361 1.000000 333
##
## $gains_lift_table
## Gains/Lift Table: Avg response rate: 16.26 %, avg score: 18.45 %
## group cumulative_data_fraction lower_threshold lift cumulative_lift
## 1 1 0.01084011 0.832069 6.150000 6.150000
## 2 2 0.02168022 0.764136 4.612500 5.381250
## 3 3 0.03252033 0.741767 6.150000 5.637500
## 4 4 0.04065041 0.694872 6.150000 5.740000
## 5 5 0.05149051 0.667062 1.537500 4.855263
## 6 6 0.10027100 0.508513 3.758333 4.321622
## 7 7 0.15176152 0.402314 1.942105 3.514286
## 8 8 0.20054201 0.305155 2.391667 3.241216
## 9 9 0.30081301 0.220540 0.997297 2.493243
## 10 10 0.40108401 0.144400 0.664865 2.036149
## 11 11 0.50135501 0.101989 0.166216 1.662162
## 12 12 0.59891599 0.070369 0.512500 1.474887
## 13 13 0.69918699 0.042530 0.498649 1.334884
## 14 14 0.79945799 0.027457 0.332432 1.209153
## 15 15 0.89972900 0.013169 0.166216 1.092922
## 16 16 1.00000000 0.001307 0.166216 1.000000
## response_rate score cumulative_response_rate cumulative_score
## 1 1.000000 0.894605 1.000000 0.894605
## 2 0.750000 0.788268 0.875000 0.841436
## 3 1.000000 0.752144 0.916667 0.811672
## 4 1.000000 0.710666 0.933333 0.791471
## 5 0.250000 0.684720 0.789474 0.768997
## 6 0.611111 0.566749 0.702703 0.670606
## 7 0.315789 0.461224 0.571429 0.599566
## 8 0.388889 0.353137 0.527027 0.539624
## 9 0.162162 0.256543 0.405405 0.445264
## 10 0.108108 0.180668 0.331081 0.379115
## 11 0.027027 0.122835 0.270270 0.327859
## 12 0.083333 0.084634 0.239819 0.288238
## 13 0.081081 0.054841 0.217054 0.254767
## 14 0.054054 0.035727 0.196610 0.227294
## 15 0.027027 0.020368 0.177711 0.204233
## 16 0.027027 0.007472 0.162602 0.184504
## capture_rate cumulative_capture_rate gain cumulative_gain
## 1 0.066667 0.066667 515.000000 515.000000
## 2 0.050000 0.116667 361.250000 438.125000
## 3 0.066667 0.183333 515.000000 463.750000
## 4 0.050000 0.233333 515.000000 474.000000
## 5 0.016667 0.250000 53.750000 385.526316
## 6 0.183333 0.433333 275.833333 332.162162
## 7 0.100000 0.533333 94.210526 251.428571
## 8 0.116667 0.650000 139.166667 224.121622
## 9 0.100000 0.750000 -0.270270 149.324324
## 10 0.066667 0.816667 -33.513514 103.614865
## 11 0.016667 0.833333 -83.378378 66.216216
## 12 0.050000 0.883333 -48.750000 47.488688
## 13 0.050000 0.933333 -50.135135 33.488372
## 14 0.033333 0.966667 -66.756757 20.915254
## 15 0.016667 0.983333 -83.378378 9.292169
## 16 0.016667 1.000000 -83.378378 0.000000
## kolmogorov_smirnov
## 1 0.066667
## 2 0.113430
## 3 0.180097
## 4 0.230097
## 5 0.237055
## 6 0.397735
## 7 0.455663
## 8 0.536731
## 9 0.536408
## 10 0.496278
## 11 0.396440
## 12 0.339644
## 13 0.279612
## 14 0.199676
## 15 0.099838
## 16 0.000000
##
## $residual_deviance
## [1] 238.4335
##
## $null_deviance
## [1] 327.9212
##
## $AIC
## [1] 250.4335
##
## $loglikelihood
## [1] 0
##
## $null_degrees_of_freedom
## [1] 368
##
## $residual_degrees_of_freedom
## [1] 363h2o.auc(performance_h2o)## [1] 0.8370011h2o.confusionMatrix(performance_h2o)## Confusion Matrix (vertical: actual; across: predicted) for max f1 @ threshold = 0.327281159662961:
## No Yes Error Rate
## No 278 31 0.100324 =31/309
## Yes 21 39 0.350000 =21/60
## Totals 299 70 0.140921 =52/369h2o.metric(performance_h2o)## Metrics for Thresholds: Binomial metrics as a function of classification thresholds
## threshold f1 f2 f0point5 accuracy precision recall specificity
## 1 0.944826 0.032787 0.020747 0.078125 0.840108 1.000000 0.016667 1.000000
## 2 0.915797 0.064516 0.041322 0.147059 0.842818 1.000000 0.033333 1.000000
## 3 0.884152 0.095238 0.061728 0.208333 0.845528 1.000000 0.050000 1.000000
## 4 0.833643 0.125000 0.081967 0.263158 0.848238 1.000000 0.066667 1.000000
## 5 0.831328 0.153846 0.102041 0.312500 0.850949 1.000000 0.083333 1.000000
## 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 0.237568 0.066667 0.533333 309 56 0 4
## 5 0.265973 0.083333 0.541667 309 55 0 5
## 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 1.000000 0.933333 0.000000 0.066667 3
## 5 1.000000 0.916667 0.000000 0.083333 4
##
## ---
## threshold f1 f2 f0point5 accuracy precision recall
## 364 0.002934 0.283019 0.496689 0.197889 0.176152 0.164835 1.000000
## 365 0.002767 0.282353 0.495868 0.197368 0.173442 0.164384 1.000000
## 366 0.002288 0.281690 0.495050 0.196850 0.170732 0.163934 1.000000
## 367 0.001886 0.281030 0.494234 0.196335 0.168022 0.163488 1.000000
## 368 0.001804 0.280374 0.493421 0.195822 0.165312 0.163043 1.000000
## 369 0.001307 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