# LOGISTIK MULTINOMIAL (PREDIKSI PEKERJAAN DARI DATA)
# Membaca dataset melalui penyimpanan
library(readr)
adult_data_clean <- read_csv("C:/Users/hp/OneDrive/Documents/adult_data_clean.csv")
## Rows: 32561 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): marital.status, sex, occupation
## dbl (3): age, education.num, hours.per.week
##
## 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.
adult_data <- adult_data_clean
View(adult_data_clean)
# Mengecek nama kolom
names(adult_data_clean)
## [1] "age" "education.num" "marital.status" "sex"
## [5] "hours.per.week" "occupation"
# Ringkasan data
summary(adult_data_clean)
## age education.num marital.status sex
## Min. :17.00 Min. : 1.00 Length:32561 Length:32561
## 1st Qu.:28.00 1st Qu.: 9.00 Class :character Class :character
## Median :37.00 Median :10.00 Mode :character Mode :character
## Mean :38.58 Mean :10.08
## 3rd Qu.:48.00 3rd Qu.:12.00
## Max. :90.00 Max. :16.00
## hours.per.week occupation
## Min. : 1.00 Length:32561
## 1st Qu.:40.00 Class :character
## Median :40.00 Mode :character
## Mean :40.44
## 3rd Qu.:45.00
## Max. :99.00
# Distribusi target variabel plot
adult_data_clean %>%
count(occupation) %>%
ggplot(aes(x = reorder(occupation, n), y = n)) +
geom_col(fill = "steelblue") +
coord_flip() +
labs(title = "Distribusi Occupation", x = "Occupation", y = "Jumlah")
# Split data
set.seed(123)
train_index <- createDataPartition(adult_data$occupation, p = 0.7, list = FALSE)
train_data <- adult_data_clean[train_index, ]
test_data <- adult_data_clean[-train_index, ]
# Model
model_multinom <- multinom(occupation ~ age + education.num + marital.status + sex + hours.per.week,
data = train_data)
## # weights: 180 (154 variable)
## initial value 61740.836535
## iter 10 value 56314.295430
## iter 20 value 55284.067962
## iter 30 value 52681.802333
## iter 40 value 51313.048240
## iter 50 value 50597.879524
## iter 60 value 49151.138958
## iter 70 value 48248.950157
## iter 80 value 47957.785452
## iter 90 value 47739.298683
## iter 100 value 47641.157158
## final value 47641.157158
## stopped after 100 iterations
summary(model_multinom)
## Call:
## multinom(formula = occupation ~ age + education.num + marital.status +
## sex + hours.per.week, data = train_data)
##
## Coefficients:
## (Intercept) age education.num
## Adm-clerical -0.8349347 -0.017874102 0.1848445
## Armed-Forces -4.9645775 -0.006695451 0.1086584
## Craft-repair -0.4800921 -0.029930309 -0.1049928
## Exec-managerial -6.1346708 -0.005137259 0.4354249
## Farming-fishing -4.3905996 -0.001497207 -0.1791137
## Handlers-cleaners 1.6413098 -0.054590713 -0.2380770
## Machine-op-inspct 1.2020655 -0.031997854 -0.2070204
## Other-service 2.6361991 -0.026407911 -0.1388879
## Priv-house-serv -0.4158676 -0.004959691 -0.2156895
## Prof-specialty -9.7124257 -0.016007118 0.8651203
## Protective-serv -4.4625104 -0.026812980 0.1659482
## Sales -2.3378696 -0.023833923 0.2007721
## Tech-support -4.4185472 -0.032128021 0.3984103
## Transport-moving -1.8770104 -0.021946767 -0.1738524
## marital.statusMarried-AF-spouse
## Adm-clerical 0.986419834
## Armed-Forces 0.369310243
## Craft-repair 1.373153894
## Exec-managerial 0.008139829
## Farming-fishing 1.967659857
## Handlers-cleaners -1.330066048
## Machine-op-inspct -2.435297670
## Other-service 1.735624309
## Priv-house-serv -0.106157561
## Prof-specialty 1.249653321
## Protective-serv -0.981045291
## Sales 0.317751953
## Tech-support -1.599731050
## Transport-moving 1.180239741
## marital.statusMarried-civ-spouse
## Adm-clerical -0.51496953
## Armed-Forces -0.25624017
## Craft-repair -0.37590953
## Exec-managerial -0.03190089
## Farming-fishing 0.07189970
## Handlers-cleaners -0.83487267
## Machine-op-inspct -0.17725814
## Other-service -0.65063652
## Priv-house-serv -0.37774193
## Prof-specialty -0.03688218
## Protective-serv -0.41352407
## Sales 0.07450942
## Tech-support -0.42073187
## Transport-moving -0.25103132
## marital.statusMarried-spouse-absent
## Adm-clerical -0.7569686
## Armed-Forces -1.1147077
## Craft-repair -0.7904695
## Exec-managerial -1.0393555
## Farming-fishing 0.5100789
## Handlers-cleaners -0.7370363
## Machine-op-inspct -0.7908065
## Other-service -0.4533636
## Priv-house-serv -0.3334036
## Prof-specialty -0.9456566
## Protective-serv -0.4969938
## Sales -0.4407889
## Tech-support -1.1654629
## Transport-moving -0.7753468
## marital.statusNever-married marital.statusSeparated
## Adm-clerical -0.7216796 -0.37552764
## Armed-Forces -0.6805239 0.02930884
## Craft-repair -1.4967965 -0.17511251
## Exec-managerial -1.1413164 -0.50544265
## Farming-fishing -0.1194970 -0.04675080
## Handlers-cleaners -0.8852337 -0.20496641
## Machine-op-inspct -1.0464660 -0.18913386
## Other-service -0.5327498 -0.02622882
## Priv-house-serv -0.4807472 -0.01934634
## Prof-specialty -0.7244859 0.02593403
## Protective-serv -1.2991466 -0.46979780
## Sales -0.4409322 -0.23978223
## Tech-support -1.0086746 -0.68096577
## Transport-moving -1.1568607 -0.14761247
## marital.statusWidowed sexMale hours.per.week
## Adm-clerical -0.63038155 -0.98532526 0.04056410
## Armed-Forces -0.18279037 0.46173250 0.04662125
## Craft-repair -0.98568152 2.49069394 0.05819179
## Exec-managerial -0.82746058 0.01523794 0.07910132
## Farming-fishing -0.07800423 2.24709415 0.09339368
## Handlers-cleaners -0.40650593 2.03611569 0.03842396
## Machine-op-inspct -0.96787978 0.57431359 0.05813295
## Other-service -0.33924029 -0.20759421 0.02336124
## Priv-house-serv -0.15574796 -1.51446517 0.03527750
## Prof-specialty -0.40851245 -0.36059304 0.05416004
## Protective-serv -0.22181181 1.62552294 0.06219172
## Sales -0.21664225 0.15023941 0.05650554
## Tech-support -0.92508826 0.09197010 0.04071757
## Transport-moving -0.47275512 2.34047733 0.07505465
##
## Std. Errors:
## (Intercept) age education.num
## Adm-clerical 0.2540541 0.002927461 0.01715281
## Armed-Forces 0.8136448 0.009194666 0.05244171
## Craft-repair 0.2663537 0.002932346 0.01639413
## Exec-managerial 0.2673840 0.002965630 0.01726941
## Farming-fishing 0.3899972 0.004075902 0.02152441
## Handlers-cleaners 0.3271280 0.004180308 0.02013334
## Machine-op-inspct 0.2866722 0.003381106 0.01804653
## Other-service 0.2518073 0.002955983 0.01651688
## Priv-house-serv 0.6096550 0.007353732 0.03748696
## Prof-specialty 0.2908228 0.003171572 0.01934742
## Protective-serv 0.4283867 0.004987163 0.02684394
## Sales 0.2603910 0.002975345 0.01706372
## Tech-support 0.3683169 0.004601520 0.02467442
## Transport-moving 0.3288147 0.003597326 0.01919945
## marital.statusMarried-AF-spouse
## Adm-clerical 0.632734174
## Armed-Forces 0.009867798
## Craft-repair 0.648087118
## Exec-managerial 0.965090887
## Farming-fishing 1.038961651
## Handlers-cleaners 0.006665915
## Machine-op-inspct 0.004844948
## Other-service 0.610838707
## Priv-house-serv 0.012631124
## Prof-specialty 0.709055251
## Protective-serv 0.006343466
## Sales 0.972186108
## Tech-support 0.014781447
## Transport-moving 1.007286245
## marital.statusMarried-civ-spouse
## Adm-clerical 0.1243570
## Armed-Forces 0.3560091
## Craft-repair 0.1268251
## Exec-managerial 0.1255286
## Farming-fishing 0.1930398
## Handlers-cleaners 0.1634851
## Machine-op-inspct 0.1396753
## Other-service 0.1295016
## Priv-house-serv 0.3040537
## Prof-specialty 0.1323833
## Protective-serv 0.1853637
## Sales 0.1289400
## Tech-support 0.1664766
## Transport-moving 0.1516724
## marital.statusMarried-spouse-absent
## Adm-clerical 0.2908616
## Armed-Forces 1.2687472
## Craft-repair 0.3144996
## Exec-managerial 0.3241983
## Farming-fishing 0.3901603
## Handlers-cleaners 0.3942271
## Machine-op-inspct 0.3481436
## Other-service 0.2900886
## Priv-house-serv 0.6266651
## Prof-specialty 0.3351115
## Protective-serv 0.4832369
## Sales 0.3090398
## Tech-support 0.4969451
## Transport-moving 0.4003460
## marital.statusNever-married marital.statusSeparated
## Adm-clerical 0.1244880 0.2112016
## Armed-Forces 0.3821768 0.6205694
## Craft-repair 0.1354916 0.2303938
## Exec-managerial 0.1319083 0.2347155
## Farming-fishing 0.2052780 0.3882345
## Handlers-cleaners 0.1687462 0.2887891
## Machine-op-inspct 0.1474263 0.2424928
## Other-service 0.1291436 0.2112408
## Priv-house-serv 0.2852605 0.4143509
## Prof-specialty 0.1364059 0.2409536
## Protective-serv 0.2071152 0.3926454
## Sales 0.1321362 0.2316492
## Tech-support 0.1736051 0.3408656
## Transport-moving 0.1661292 0.2875544
## marital.statusWidowed sexMale hours.per.week
## Adm-clerical 0.1777670 0.08038789 0.002964958
## Armed-Forces 0.5970140 0.26470608 0.009784564
## Craft-repair 0.2694432 0.10924602 0.003039935
## Exec-managerial 0.2091960 0.08437822 0.003018404
## Farming-fishing 0.3693034 0.18510254 0.003858403
## Handlers-cleaners 0.3516887 0.12682350 0.003893534
## Machine-op-inspct 0.2678836 0.09483903 0.003478143
## Other-service 0.1808440 0.07933768 0.002954050
## Priv-house-serv 0.3542169 0.24058029 0.007867546
## Prof-specialty 0.2188306 0.08612323 0.003131445
## Protective-serv 0.3998359 0.17121209 0.004927429
## Sales 0.2020239 0.08088936 0.003014380
## Tech-support 0.3605436 0.11424632 0.004411298
## Transport-moving 0.3208624 0.15120280 0.003614069
##
## Residual Deviance: 95282.31
## AIC: 95590.31
# Prediksi pada data test
test_data$occupation <- factor(test_data$occupation)
prediksi <- predict(model_multinom, newdata = test_data)
# Lalu buat confusion matrix
library(caret)
conf_mat <- confusionMatrix(prediksi, test_data$occupation)
conf_mat
## Confusion Matrix and Statistics
##
## Reference
## Prediction ? Adm-clerical Armed-Forces Craft-repair Exec-managerial
## ? 46 14 0 7 11
## Adm-clerical 124 599 0 31 168
## Armed-Forces 0 0 0 0 0
## Craft-repair 156 199 2 925 323
## Exec-managerial 28 38 0 78 214
## Farming-fishing 2 2 0 5 2
## Handlers-cleaners 19 0 0 19 0
## Machine-op-inspct 2 0 0 1 1
## Other-service 103 87 0 33 11
## Priv-house-serv 0 0 0 0 0
## Prof-specialty 65 177 0 74 465
## Protective-serv 0 0 0 0 0
## Sales 7 15 0 56 24
## Tech-support 0 0 0 0 0
## Transport-moving 0 0 0 0 0
## Reference
## Prediction Farming-fishing Handlers-cleaners Machine-op-inspct
## ? 4 5 3
## Adm-clerical 11 34 108
## Armed-Forces 0 0 0
## Craft-repair 187 255 375
## Exec-managerial 40 11 10
## Farming-fishing 9 1 2
## Handlers-cleaners 19 19 13
## Machine-op-inspct 1 0 3
## Other-service 12 59 63
## Priv-house-serv 0 0 0
## Prof-specialty 13 16 15
## Protective-serv 0 0 0
## Sales 2 11 8
## Tech-support 0 0 0
## Transport-moving 0 0 0
## Reference
## Prediction Other-service Priv-house-serv Prof-specialty
## ? 20 2 10
## Adm-clerical 309 16 103
## Armed-Forces 0 0 0
## Craft-repair 255 0 121
## Exec-managerial 22 0 113
## Farming-fishing 5 0 2
## Handlers-cleaners 32 1 1
## Machine-op-inspct 4 0 0
## Other-service 253 24 19
## Priv-house-serv 0 0 0
## Prof-specialty 67 1 862
## Protective-serv 0 0 0
## Sales 21 0 11
## Tech-support 0 0 0
## Transport-moving 0 0 0
## Reference
## Prediction Protective-serv Sales Tech-support Transport-moving
## ? 2 19 3 5
## Adm-clerical 16 233 73 19
## Armed-Forces 0 0 0 0
## Craft-repair 111 347 84 375
## Exec-managerial 29 126 23 23
## Farming-fishing 1 2 0 9
## Handlers-cleaners 1 3 0 7
## Machine-op-inspct 1 1 0 0
## Other-service 5 131 13 8
## Priv-house-serv 0 0 0 0
## Prof-specialty 25 206 74 20
## Protective-serv 0 0 0 0
## Sales 3 27 8 13
## Tech-support 0 0 0 0
## Transport-moving 0 0 0 0
##
## Overall Statistics
##
## Accuracy : 0.3029
## 95% CI : (0.2938, 0.3121)
## No Information Rate : 0.1272
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 0.2086
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: ? Class: Adm-clerical Class: Armed-Forces
## Sensitivity 0.083333 0.52962 0.0000000
## Specificity 0.988599 0.85575 1.0000000
## Pos Pred Value 0.304636 0.32484 NaN
## Neg Pred Value 0.947352 0.93281 0.9997951
## Prevalence 0.056546 0.11586 0.0002049
## Detection Rate 0.004712 0.06136 0.0000000
## Detection Prevalence 0.015468 0.18890 0.0000000
## Balanced Accuracy 0.535966 0.69269 0.5000000
## Class: Craft-repair Class: Exec-managerial
## Sensitivity 0.75264 0.17555
## Specificity 0.67303 0.93667
## Pos Pred Value 0.24899 0.28344
## Neg Pred Value 0.94973 0.88842
## Prevalence 0.12590 0.12487
## Detection Rate 0.09476 0.02192
## Detection Prevalence 0.38056 0.07734
## Balanced Accuracy 0.71284 0.55611
## Class: Farming-fishing Class: Handlers-cleaners
## Sensitivity 0.0302013 0.046229
## Specificity 0.9965131 0.987702
## Pos Pred Value 0.2142857 0.141791
## Neg Pred Value 0.9702675 0.959285
## Prevalence 0.0305265 0.042102
## Detection Rate 0.0009219 0.001946
## Detection Prevalence 0.0043024 0.013727
## Balanced Accuracy 0.5133572 0.516965
## Class: Machine-op-inspct Class: Other-service
## Sensitivity 0.0050000 0.25607
## Specificity 0.9987994 0.93526
## Pos Pred Value 0.2142857 0.30816
## Neg Pred Value 0.9387567 0.91779
## Prevalence 0.0614628 0.10121
## Detection Rate 0.0003073 0.02592
## Detection Prevalence 0.0014341 0.08410
## Balanced Accuracy 0.5018997 0.59567
## Class: Priv-house-serv Class: Prof-specialty
## Sensitivity 0.000000 0.6940
## Specificity 1.000000 0.8570
## Pos Pred Value NaN 0.4144
## Neg Pred Value 0.995493 0.9505
## Prevalence 0.004507 0.1272
## Detection Rate 0.000000 0.0883
## Detection Prevalence 0.000000 0.2131
## Balanced Accuracy 0.500000 0.7755
## Class: Protective-serv Class: Sales Class: Tech-support
## Sensitivity 0.00000 0.024658 0.00000
## Specificity 1.00000 0.979347 1.00000
## Pos Pred Value NaN 0.131068 NaN
## Neg Pred Value 0.98013 0.888238 0.97152
## Prevalence 0.01987 0.112170 0.02848
## Detection Rate 0.00000 0.002766 0.00000
## Detection Prevalence 0.00000 0.021102 0.00000
## Balanced Accuracy 0.50000 0.502002 0.50000
## Class: Transport-moving
## Sensitivity 0.00000
## Specificity 1.00000
## Pos Pred Value NaN
## Neg Pred Value 0.95093
## Prevalence 0.04907
## Detection Rate 0.00000
## Detection Prevalence 0.00000
## Balanced Accuracy 0.50000
conf_mat$overall['Akurasi']
## <NA>
## NA
# Interpretasi, Ekstrak koefisien
coefs <- summary(model_multinom)$coefficients
coefs
## (Intercept) age education.num
## Adm-clerical -0.8349347 -0.017874102 0.1848445
## Armed-Forces -4.9645775 -0.006695451 0.1086584
## Craft-repair -0.4800921 -0.029930309 -0.1049928
## Exec-managerial -6.1346708 -0.005137259 0.4354249
## Farming-fishing -4.3905996 -0.001497207 -0.1791137
## Handlers-cleaners 1.6413098 -0.054590713 -0.2380770
## Machine-op-inspct 1.2020655 -0.031997854 -0.2070204
## Other-service 2.6361991 -0.026407911 -0.1388879
## Priv-house-serv -0.4158676 -0.004959691 -0.2156895
## Prof-specialty -9.7124257 -0.016007118 0.8651203
## Protective-serv -4.4625104 -0.026812980 0.1659482
## Sales -2.3378696 -0.023833923 0.2007721
## Tech-support -4.4185472 -0.032128021 0.3984103
## Transport-moving -1.8770104 -0.021946767 -0.1738524
## marital.statusMarried-AF-spouse
## Adm-clerical 0.986419834
## Armed-Forces 0.369310243
## Craft-repair 1.373153894
## Exec-managerial 0.008139829
## Farming-fishing 1.967659857
## Handlers-cleaners -1.330066048
## Machine-op-inspct -2.435297670
## Other-service 1.735624309
## Priv-house-serv -0.106157561
## Prof-specialty 1.249653321
## Protective-serv -0.981045291
## Sales 0.317751953
## Tech-support -1.599731050
## Transport-moving 1.180239741
## marital.statusMarried-civ-spouse
## Adm-clerical -0.51496953
## Armed-Forces -0.25624017
## Craft-repair -0.37590953
## Exec-managerial -0.03190089
## Farming-fishing 0.07189970
## Handlers-cleaners -0.83487267
## Machine-op-inspct -0.17725814
## Other-service -0.65063652
## Priv-house-serv -0.37774193
## Prof-specialty -0.03688218
## Protective-serv -0.41352407
## Sales 0.07450942
## Tech-support -0.42073187
## Transport-moving -0.25103132
## marital.statusMarried-spouse-absent
## Adm-clerical -0.7569686
## Armed-Forces -1.1147077
## Craft-repair -0.7904695
## Exec-managerial -1.0393555
## Farming-fishing 0.5100789
## Handlers-cleaners -0.7370363
## Machine-op-inspct -0.7908065
## Other-service -0.4533636
## Priv-house-serv -0.3334036
## Prof-specialty -0.9456566
## Protective-serv -0.4969938
## Sales -0.4407889
## Tech-support -1.1654629
## Transport-moving -0.7753468
## marital.statusNever-married marital.statusSeparated
## Adm-clerical -0.7216796 -0.37552764
## Armed-Forces -0.6805239 0.02930884
## Craft-repair -1.4967965 -0.17511251
## Exec-managerial -1.1413164 -0.50544265
## Farming-fishing -0.1194970 -0.04675080
## Handlers-cleaners -0.8852337 -0.20496641
## Machine-op-inspct -1.0464660 -0.18913386
## Other-service -0.5327498 -0.02622882
## Priv-house-serv -0.4807472 -0.01934634
## Prof-specialty -0.7244859 0.02593403
## Protective-serv -1.2991466 -0.46979780
## Sales -0.4409322 -0.23978223
## Tech-support -1.0086746 -0.68096577
## Transport-moving -1.1568607 -0.14761247
## marital.statusWidowed sexMale hours.per.week
## Adm-clerical -0.63038155 -0.98532526 0.04056410
## Armed-Forces -0.18279037 0.46173250 0.04662125
## Craft-repair -0.98568152 2.49069394 0.05819179
## Exec-managerial -0.82746058 0.01523794 0.07910132
## Farming-fishing -0.07800423 2.24709415 0.09339368
## Handlers-cleaners -0.40650593 2.03611569 0.03842396
## Machine-op-inspct -0.96787978 0.57431359 0.05813295
## Other-service -0.33924029 -0.20759421 0.02336124
## Priv-house-serv -0.15574796 -1.51446517 0.03527750
## Prof-specialty -0.40851245 -0.36059304 0.05416004
## Protective-serv -0.22181181 1.62552294 0.06219172
## Sales -0.21664225 0.15023941 0.05650554
## Tech-support -0.92508826 0.09197010 0.04071757
## Transport-moving -0.47275512 2.34047733 0.07505465
exp(coefs)
## (Intercept) age education.num
## Adm-clerical 4.339028e-01 0.9822847 1.2030314
## Armed-Forces 6.980900e-03 0.9933269 1.1147815
## Craft-repair 6.187264e-01 0.9705132 0.9003310
## Exec-managerial 2.166438e-03 0.9948759 1.5456196
## Farming-fishing 1.239330e-02 0.9985039 0.8360109
## Handlers-cleaners 5.161926e+00 0.9468726 0.7881420
## Machine-op-inspct 3.326982e+00 0.9685087 0.8130030
## Other-service 1.396004e+01 0.9739377 0.8703256
## Priv-house-serv 6.597676e-01 0.9950526 0.8059856
## Prof-specialty 6.052672e-05 0.9841203 2.3752919
## Protective-serv 1.153337e-02 0.9735433 1.1805120
## Sales 9.653307e-02 0.9764479 1.2223462
## Tech-support 1.205173e-02 0.9683826 1.4894550
## Transport-moving 1.530470e-01 0.9782923 0.8404210
## marital.statusMarried-AF-spouse
## Adm-clerical 2.68161663
## Armed-Forces 1.44673637
## Craft-repair 3.94778197
## Exec-managerial 1.00817305
## Farming-fishing 7.15391570
## Handlers-cleaners 0.26445979
## Machine-op-inspct 0.08757168
## Other-service 5.67246807
## Priv-house-serv 0.89928294
## Prof-specialty 3.48913314
## Protective-serv 0.37491899
## Sales 1.37403539
## Tech-support 0.20195083
## Transport-moving 3.25515450
## marital.statusMarried-civ-spouse
## Adm-clerical 0.5975188
## Armed-Forces 0.7739561
## Craft-repair 0.6866644
## Exec-managerial 0.9686026
## Farming-fishing 1.0745476
## Handlers-cleaners 0.4339297
## Machine-op-inspct 0.8375635
## Other-service 0.5217136
## Priv-house-serv 0.6854074
## Prof-specialty 0.9637897
## Protective-serv 0.6613156
## Sales 1.0773555
## Tech-support 0.6565661
## Transport-moving 0.7779980
## marital.statusMarried-spouse-absent
## Adm-clerical 0.4690863
## Armed-Forces 0.3280111
## Craft-repair 0.4536317
## Exec-managerial 0.3536826
## Farming-fishing 1.6654225
## Handlers-cleaners 0.4785300
## Machine-op-inspct 0.4534789
## Other-service 0.6354870
## Priv-house-serv 0.7164810
## Prof-specialty 0.3884245
## Protective-serv 0.6083568
## Sales 0.6435285
## Tech-support 0.3117783
## Transport-moving 0.4605440
## marital.statusNever-married marital.statusSeparated
## Adm-clerical 0.4859354 0.6869267
## Armed-Forces 0.5063517 1.0297426
## Craft-repair 0.2238461 0.8393626
## Exec-managerial 0.3193983 0.6032385
## Farming-fishing 0.8873667 0.9543252
## Handlers-cleaners 0.4126177 0.8146747
## Machine-op-inspct 0.3511766 0.8276757
## Other-service 0.5869886 0.9741122
## Priv-house-serv 0.6183212 0.9808396
## Prof-specialty 0.4845736 1.0262732
## Protective-serv 0.2727645 0.6251287
## Sales 0.6434363 0.7867992
## Tech-support 0.3647020 0.5061280
## Transport-moving 0.3144719 0.8627654
## marital.statusWidowed sexMale hours.per.week
## Adm-clerical 0.5323886 0.3733178 1.041398
## Armed-Forces 0.8329427 1.5868208 1.047725
## Craft-repair 0.3731848 12.0696488 1.059918
## Exec-managerial 0.4371580 1.0153546 1.082314
## Farming-fishing 0.9249605 9.4602059 1.097894
## Handlers-cleaners 0.6659731 7.6607944 1.039172
## Machine-op-inspct 0.3798876 1.7759111 1.059856
## Other-service 0.7123113 0.8125367 1.023636
## Priv-house-serv 0.8557749 0.2199258 1.035907
## Prof-specialty 0.6646382 0.6972627 1.055654
## Protective-serv 0.8010661 5.0810755 1.064166
## Sales 0.8052180 1.1621124 1.058132
## Tech-support 0.3964964 1.0963320 1.041558
## Transport-moving 0.6232827 10.3861930 1.077943