8 k-Nearest Neighbors
8.0.1 Reminders about the Data
tibble::tribble(
~"Variable in Data", ~"Definition", ~"Data Type",
'seqn', 'Respondent sequence number', 'Identifier',
'riagendr', 'Gender', 'Categorical',
'ridageyr', 'Age in years at screening', 'Continuous / Numerical',
'ridreth1', 'Race/Hispanic origin', 'Categorical',
'dmdeduc2', 'Education level', 'Adults 20+ - Categorical',
'dmdmartl', 'Marital status', 'Categorical',
'indhhin2', 'Annual household income', 'Categorical',
'bmxbmi', 'Body Mass Index (kg/m**2)', 'Continuous / Numerical',
'diq010', 'Doctor diagnosed diabetes', 'Categorical',
'lbxglu', 'Fasting Glucose (mg/dL)', 'Continuous / Numerical'
) |>
knitr::kable()| Variable in Data | Definition | Data Type |
|---|---|---|
| seqn | Respondent sequence number | Identifier |
| riagendr | Gender | Categorical |
| ridageyr | Age in years at screening | Continuous / Numerical |
| ridreth1 | Race/Hispanic origin | Categorical |
| dmdeduc2 | Education level | Adults 20+ - Categorical |
| dmdmartl | Marital status | Categorical |
| indhhin2 | Annual household income | Categorical |
| bmxbmi | Body Mass Index (kg/m**2) | Continuous / Numerical |
| diq010 | Doctor diagnosed diabetes | Categorical |
| lbxglu | Fasting Glucose (mg/dL) | Continuous / Numerical |
8.0.2 Install if not Function
install_if_not <- function( list.of.packages ) {
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) { install.packages(new.packages) } else { print(paste0("the package '", list.of.packages , "' is already installed")) }
}8.1 Split Data into Training and Test Sets.
── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.4 ✔ readr 2.1.5
✔ forcats 1.0.0 ✔ stringr 1.5.1
✔ ggplot2 3.5.1 ✔ tibble 3.2.1
✔ lubridate 1.9.3 ✔ tidyr 1.3.1
✔ purrr 1.0.2
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsLoading required package: lattice
Attaching package: 'caret'
The following object is masked from 'package:purrr':
lift# this will ensure our results are the same every run, to randomize you may use: set.seed(Sys.time())
set.seed(8675309)
# The createDataPartition function is used to create training and test sets
trainIndex <- createDataPartition(diab_pop.no_na_vals$diq010,
p = .6,
list = FALSE,
times = 1)
dm2.train <- diab_pop.no_na_vals[trainIndex, ]
dm2.test <- diab_pop.no_na_vals[-trainIndex, ]
8.2 Make dummyVars
Often dummyVars or one-hot-encodings of categorical features are required for models:
8.3 findLinearCombos
For many models, we can find and remove features that are linear combinations of one another:
comboInformation <- findLinearCombos(dm2Train_dummies)
comboInformation$linearCombos
$linearCombos[[1]]
[1] 9 2 3 5 6 7 8
$linearCombos[[2]]
[1] 14 2 3 10 11 12 13
$linearCombos[[3]]
[1] 20 2 3 15 16 17 18 19
$linearCombos[[4]]
[1] 27
$linearCombos[[5]]
[1] 29
$linearCombos[[6]]
[1] 34 2 3 21 22 23 24 25 26 28 30 31 32 33
$linearCombos[[7]]
[1] 37 2 3 36
$remove
[1] 9 14 20 27 29 34 37dm2Train_dummies_independent <- dm2Train_dummies[,-comboInformation$remove ]
8.4 findCorrelation
Similarly, for many models, we will want to find and remove features that are highly correlated with one another:
features1 <- colnames(dm2Train_dummies_independent)[!colnames(dm2Train_dummies_independent) %in% c("seqn", "diq010.Diabetes")]
features1 [1] "riagendr.Male" "riagendr.Female"
[3] "ridageyr" "ridreth1.MexicanAmerican"
[5] "ridreth1.Other Hispanic" "ridreth1.Non-Hispanic White"
[7] "ridreth1.Non-Hispanic Black" "dmdeduc2.Less than 9th grade"
[9] "dmdeduc2.Grades 9-11th" "dmdeduc2.High school graduate/GED"
[11] "dmdeduc2.Some college or AA degrees" "dmdmartl.Married"
[13] "dmdmartl.Widowed" "dmdmartl.Divorced"
[15] "dmdmartl.Separated" "dmdmartl.Never married"
[17] "indhhin2.$0-$4,999" "indhhin2.$5,000-$9,999"
[19] "indhhin2.$10,000-$14,999" "indhhin2.$15,000-$19,999"
[21] "indhhin2.$20,000-$24,999" "indhhin2.$25,000-$34,999"
[23] "indhhin2.$45,000-$54,999" "indhhin2.$65,000-$74,999"
[25] "indhhin2.20,000+" "indhhin2.less than $20,000"
[27] "indhhin2.$75,000-$99,999" "bmxbmi"
[29] "lbxglu" cor_matrix <- cor(dm2Train_dummies_independent[, features1])
cor_matrix riagendr.Male riagendr.Female ridageyr
riagendr.Male 1.0000000000 -1.0000000000 0.046088493
riagendr.Female -1.0000000000 1.0000000000 -0.046088493
ridageyr 0.0460884932 -0.0460884932 1.000000000
ridreth1.MexicanAmerican -0.0085703283 0.0085703283 -0.022071061
ridreth1.Other Hispanic -0.0412627007 0.0412627007 0.026956617
ridreth1.Non-Hispanic White 0.0310058641 -0.0310058641 0.156358779
ridreth1.Non-Hispanic Black -0.0372062109 0.0372062109 -0.075988046
dmdeduc2.Less than 9th grade -0.0009220555 0.0009220555 0.147823014
dmdeduc2.Grades 9-11th 0.0676150181 -0.0676150181 -0.017679412
dmdeduc2.High school graduate/GED -0.0077741175 0.0077741175 0.002357556
dmdeduc2.Some college or AA degrees -0.0463613674 0.0463613674 -0.043787659
dmdmartl.Married 0.0882752805 -0.0882752805 0.105297351
dmdmartl.Widowed -0.1110870515 0.1110870515 0.359280920
dmdmartl.Divorced -0.0095450138 0.0095450138 0.160349391
dmdmartl.Separated -0.0400687435 0.0400687435 0.048347775
dmdmartl.Never married -0.0084660196 0.0084660196 -0.391576503
indhhin2.$0-$4,999 0.0333800869 -0.0333800869 -0.005337734
indhhin2.$5,000-$9,999 0.0202719396 -0.0202719396 0.103081009
indhhin2.$10,000-$14,999 -0.0280078607 0.0280078607 0.099516500
indhhin2.$15,000-$19,999 -0.0082798716 0.0082798716 0.047583836
indhhin2.$20,000-$24,999 -0.0136157655 0.0136157655 0.004340429
indhhin2.$25,000-$34,999 -0.0301474637 0.0301474637 -0.023087596
indhhin2.$45,000-$54,999 -0.0014296884 0.0014296884 -0.038052431
indhhin2.$65,000-$74,999 0.0066689392 -0.0066689392 0.005053337
indhhin2.20,000+ -0.0334160423 0.0334160423 -0.081919744
indhhin2.less than $20,000 -0.0141467473 0.0141467473 0.056240125
indhhin2.$75,000-$99,999 0.0370271813 -0.0370271813 -0.085864313
bmxbmi -0.0740507417 0.0740507417 0.054940490
lbxglu 0.0525380850 -0.0525380850 0.221625646
ridreth1.MexicanAmerican
riagendr.Male -0.008570328
riagendr.Female 0.008570328
ridageyr -0.022071061
ridreth1.MexicanAmerican 1.000000000
ridreth1.Other Hispanic -0.177716978
ridreth1.Non-Hispanic White -0.321970895
ridreth1.Non-Hispanic Black -0.210901590
dmdeduc2.Less than 9th grade 0.328273872
dmdeduc2.Grades 9-11th 0.093925466
dmdeduc2.High school graduate/GED -0.025177896
dmdeduc2.Some college or AA degrees -0.115248278
dmdmartl.Married 0.067258067
dmdmartl.Widowed 0.052896641
dmdmartl.Divorced -0.047754034
dmdmartl.Separated 0.052214662
dmdmartl.Never married -0.105639997
indhhin2.$0-$4,999 -0.033075430
indhhin2.$5,000-$9,999 0.061304872
indhhin2.$10,000-$14,999 0.067602978
indhhin2.$15,000-$19,999 0.051828844
indhhin2.$20,000-$24,999 0.080518667
indhhin2.$25,000-$34,999 -0.022372295
indhhin2.$45,000-$54,999 -0.021509342
indhhin2.$65,000-$74,999 -0.019822460
indhhin2.20,000+ 0.106103297
indhhin2.less than $20,000 0.004210595
indhhin2.$75,000-$99,999 -0.058022583
bmxbmi 0.116792190
lbxglu 0.055799978
ridreth1.Other Hispanic
riagendr.Male -0.041262701
riagendr.Female 0.041262701
ridageyr 0.026956617
ridreth1.MexicanAmerican -0.177716978
ridreth1.Other Hispanic 1.000000000
ridreth1.Non-Hispanic White -0.308856305
ridreth1.Non-Hispanic Black -0.202311100
dmdeduc2.Less than 9th grade 0.094191293
dmdeduc2.Grades 9-11th 0.059711756
dmdeduc2.High school graduate/GED -0.057372094
dmdeduc2.Some college or AA degrees -0.026650804
dmdmartl.Married -0.015939096
dmdmartl.Widowed -0.032212505
dmdmartl.Divorced 0.001870325
dmdmartl.Separated 0.031938656
dmdmartl.Never married -0.035570780
indhhin2.$0-$4,999 0.030129995
indhhin2.$5,000-$9,999 0.011555790
indhhin2.$10,000-$14,999 0.068372573
indhhin2.$15,000-$19,999 0.043839553
indhhin2.$20,000-$24,999 0.010538303
indhhin2.$25,000-$34,999 -0.031578666
indhhin2.$45,000-$54,999 -0.012403737
indhhin2.$65,000-$74,999 0.007335408
indhhin2.20,000+ -0.015924383
indhhin2.less than $20,000 0.008793109
indhhin2.$75,000-$99,999 0.021713079
bmxbmi 0.031603671
lbxglu 0.053913405
ridreth1.Non-Hispanic White
riagendr.Male 0.031005864
riagendr.Female -0.031005864
ridageyr 0.156358779
ridreth1.MexicanAmerican -0.321970895
ridreth1.Other Hispanic -0.308856305
ridreth1.Non-Hispanic White 1.000000000
ridreth1.Non-Hispanic Black -0.366528210
dmdeduc2.Less than 9th grade -0.192023323
dmdeduc2.Grades 9-11th -0.085261491
dmdeduc2.High school graduate/GED 0.081203139
dmdeduc2.Some college or AA degrees 0.070062959
dmdmartl.Married 0.015294283
dmdmartl.Widowed 0.059593558
dmdmartl.Divorced 0.084753269
dmdmartl.Separated -0.050557371
dmdmartl.Never married -0.074683860
indhhin2.$0-$4,999 -0.034612554
indhhin2.$5,000-$9,999 -0.066432877
indhhin2.$10,000-$14,999 -0.060004455
indhhin2.$15,000-$19,999 -0.011209740
indhhin2.$20,000-$24,999 -0.027214942
indhhin2.$25,000-$34,999 0.040134962
indhhin2.$45,000-$54,999 0.006137821
indhhin2.$65,000-$74,999 0.013117724
indhhin2.20,000+ -0.016619728
indhhin2.less than $20,000 -0.020652926
indhhin2.$75,000-$99,999 -0.030853880
bmxbmi -0.047290232
lbxglu -0.048149775
ridreth1.Non-Hispanic Black
riagendr.Male -0.037206211
riagendr.Female 0.037206211
ridageyr -0.075988046
ridreth1.MexicanAmerican -0.210901590
ridreth1.Other Hispanic -0.202311100
ridreth1.Non-Hispanic White -0.366528210
ridreth1.Non-Hispanic Black 1.000000000
dmdeduc2.Less than 9th grade -0.078149171
dmdeduc2.Grades 9-11th -0.004177363
dmdeduc2.High school graduate/GED 0.080487107
dmdeduc2.Some college or AA degrees 0.063409064
dmdmartl.Married -0.159818914
dmdmartl.Widowed -0.037916187
dmdmartl.Divorced 0.009980058
dmdmartl.Separated 0.054691455
dmdmartl.Never married 0.192682039
indhhin2.$0-$4,999 0.058020816
indhhin2.$5,000-$9,999 0.045378363
indhhin2.$10,000-$14,999 0.027025111
indhhin2.$15,000-$19,999 -0.046330517
indhhin2.$20,000-$24,999 0.012999189
indhhin2.$25,000-$34,999 0.015608909
indhhin2.$45,000-$54,999 0.026768158
indhhin2.$65,000-$74,999 -0.002248780
indhhin2.20,000+ -0.048907525
indhhin2.less than $20,000 0.021062862
indhhin2.$75,000-$99,999 0.010089490
bmxbmi 0.106419542
lbxglu 0.011610207
dmdeduc2.Less than 9th grade
riagendr.Male -0.0009220555
riagendr.Female 0.0009220555
ridageyr 0.1478230144
ridreth1.MexicanAmerican 0.3282738722
ridreth1.Other Hispanic 0.0941912931
ridreth1.Non-Hispanic White -0.1920233230
ridreth1.Non-Hispanic Black -0.0781491710
dmdeduc2.Less than 9th grade 1.0000000000
dmdeduc2.Grades 9-11th -0.1321511581
dmdeduc2.High school graduate/GED -0.2020700027
dmdeduc2.Some college or AA degrees -0.2269233016
dmdmartl.Married -0.0107183027
dmdmartl.Widowed 0.1932578139
dmdmartl.Divorced -0.0327518205
dmdmartl.Separated 0.0788352705
dmdmartl.Never married -0.0951645902
indhhin2.$0-$4,999 0.0301543083
indhhin2.$5,000-$9,999 0.1435950588
indhhin2.$10,000-$14,999 0.1268623876
indhhin2.$15,000-$19,999 0.0700824303
indhhin2.$20,000-$24,999 0.0934027377
indhhin2.$25,000-$34,999 -0.0404739930
indhhin2.$45,000-$54,999 -0.0354900485
indhhin2.$65,000-$74,999 -0.0222554498
indhhin2.20,000+ -0.0192741216
indhhin2.less than $20,000 0.0585181739
indhhin2.$75,000-$99,999 -0.0842356013
bmxbmi 0.0124665369
lbxglu 0.0788325530
dmdeduc2.Grades 9-11th
riagendr.Male 0.067615018
riagendr.Female -0.067615018
ridageyr -0.017679412
ridreth1.MexicanAmerican 0.093925466
ridreth1.Other Hispanic 0.059711756
ridreth1.Non-Hispanic White -0.085261491
ridreth1.Non-Hispanic Black -0.004177363
dmdeduc2.Less than 9th grade -0.132151158
dmdeduc2.Grades 9-11th 1.000000000
dmdeduc2.High school graduate/GED -0.194387846
dmdeduc2.Some college or AA degrees -0.218296289
dmdmartl.Married -0.024721213
dmdmartl.Widowed -0.038120134
dmdmartl.Divorced 0.011198350
dmdmartl.Separated 0.070645079
dmdmartl.Never married -0.005731553
indhhin2.$0-$4,999 0.101146120
indhhin2.$5,000-$9,999 0.075501039
indhhin2.$10,000-$14,999 0.040530516
indhhin2.$15,000-$19,999 0.048786101
indhhin2.$20,000-$24,999 0.033182461
indhhin2.$25,000-$34,999 0.047684818
indhhin2.$45,000-$54,999 -0.038028797
indhhin2.$65,000-$74,999 -0.015752518
indhhin2.20,000+ 0.033202359
indhhin2.less than $20,000 -0.013741434
indhhin2.$75,000-$99,999 -0.025204946
bmxbmi 0.006400369
lbxglu -0.021872981
dmdeduc2.High school graduate/GED
riagendr.Male -0.007774118
riagendr.Female 0.007774118
ridageyr 0.002357556
ridreth1.MexicanAmerican -0.025177896
ridreth1.Other Hispanic -0.057372094
ridreth1.Non-Hispanic White 0.081203139
ridreth1.Non-Hispanic Black 0.080487107
dmdeduc2.Less than 9th grade -0.202070003
dmdeduc2.Grades 9-11th -0.194387846
dmdeduc2.High school graduate/GED 1.000000000
dmdeduc2.Some college or AA degrees -0.333793002
dmdmartl.Married -0.041561558
dmdmartl.Widowed 0.052185083
dmdmartl.Divorced 0.013911245
dmdmartl.Separated -0.033930172
dmdmartl.Never married 0.014957400
indhhin2.$0-$4,999 0.014936767
indhhin2.$5,000-$9,999 -0.025510668
indhhin2.$10,000-$14,999 0.066777014
indhhin2.$15,000-$19,999 0.078688206
indhhin2.$20,000-$24,999 -0.028642427
indhhin2.$25,000-$34,999 0.080105990
indhhin2.$45,000-$54,999 0.005154086
indhhin2.$65,000-$74,999 0.038227235
indhhin2.20,000+ -0.012414401
indhhin2.less than $20,000 0.007328464
indhhin2.$75,000-$99,999 -0.067298498
bmxbmi 0.059502755
lbxglu 0.038260129
dmdeduc2.Some college or AA degrees
riagendr.Male -0.046361367
riagendr.Female 0.046361367
ridageyr -0.043787659
ridreth1.MexicanAmerican -0.115248278
ridreth1.Other Hispanic -0.026650804
ridreth1.Non-Hispanic White 0.070062959
ridreth1.Non-Hispanic Black 0.063409064
dmdeduc2.Less than 9th grade -0.226923302
dmdeduc2.Grades 9-11th -0.218296289
dmdeduc2.High school graduate/GED -0.333793002
dmdeduc2.Some college or AA degrees 1.000000000
dmdmartl.Married -0.050585041
dmdmartl.Widowed -0.054164114
dmdmartl.Divorced 0.049643221
dmdmartl.Separated -0.017812444
dmdmartl.Never married 0.051589769
indhhin2.$0-$4,999 -0.061416580
indhhin2.$5,000-$9,999 -0.009211246
indhhin2.$10,000-$14,999 -0.056464018
indhhin2.$15,000-$19,999 -0.049835260
indhhin2.$20,000-$24,999 0.029408267
indhhin2.$25,000-$34,999 0.017885975
indhhin2.$45,000-$54,999 0.082630518
indhhin2.$65,000-$74,999 0.015921913
indhhin2.20,000+ -0.029218897
indhhin2.less than $20,000 0.006303120
indhhin2.$75,000-$99,999 0.024863521
bmxbmi 0.071803429
lbxglu -0.040285393
dmdmartl.Married dmdmartl.Widowed
riagendr.Male 0.08827528 -0.111087051
riagendr.Female -0.08827528 0.111087051
ridageyr 0.10529735 0.359280920
ridreth1.MexicanAmerican 0.06725807 0.052896641
ridreth1.Other Hispanic -0.01593910 -0.032212505
ridreth1.Non-Hispanic White 0.01529428 0.059593558
ridreth1.Non-Hispanic Black -0.15981891 -0.037916187
dmdeduc2.Less than 9th grade -0.01071830 0.193257814
dmdeduc2.Grades 9-11th -0.02472121 -0.038120134
dmdeduc2.High school graduate/GED -0.04156156 0.052185083
dmdeduc2.Some college or AA degrees -0.05058504 -0.054164114
dmdmartl.Married 1.00000000 -0.289838225
dmdmartl.Widowed -0.28983822 1.000000000
dmdmartl.Divorced -0.35357750 -0.099608674
dmdmartl.Separated -0.19212731 -0.054125464
dmdmartl.Never married -0.47425428 -0.133605334
indhhin2.$0-$4,999 -0.11669433 0.028159045
indhhin2.$5,000-$9,999 -0.10623715 0.138000043
indhhin2.$10,000-$14,999 -0.10652619 0.113977642
indhhin2.$15,000-$19,999 -0.13782316 0.026278741
indhhin2.$20,000-$24,999 -0.03946540 -0.002073153
indhhin2.$25,000-$34,999 -0.02589437 0.040742036
indhhin2.$45,000-$54,999 0.07449836 -0.034556429
indhhin2.$65,000-$74,999 0.05005180 -0.042747474
indhhin2.20,000+ -0.05884298 -0.051180644
indhhin2.less than $20,000 -0.05129407 0.027658261
indhhin2.$75,000-$99,999 0.06660687 -0.072779967
bmxbmi 0.02601791 0.013266915
lbxglu 0.02798044 0.047303877
dmdmartl.Divorced dmdmartl.Separated
riagendr.Male -0.009545014 -0.040068744
riagendr.Female 0.009545014 0.040068744
ridageyr 0.160349391 0.048347775
ridreth1.MexicanAmerican -0.047754034 0.052214662
ridreth1.Other Hispanic 0.001870325 0.031938656
ridreth1.Non-Hispanic White 0.084753269 -0.050557371
ridreth1.Non-Hispanic Black 0.009980058 0.054691455
dmdeduc2.Less than 9th grade -0.032751820 0.078835270
dmdeduc2.Grades 9-11th 0.011198350 0.070645079
dmdeduc2.High school graduate/GED 0.013911245 -0.033930172
dmdeduc2.Some college or AA degrees 0.049643221 -0.017812444
dmdmartl.Married -0.353577501 -0.192127306
dmdmartl.Widowed -0.099608674 -0.054125464
dmdmartl.Divorced 1.000000000 -0.066028372
dmdmartl.Separated -0.066028372 1.000000000
dmdmartl.Never married -0.162986922 -0.088564001
indhhin2.$0-$4,999 0.105461562 -0.005041179
indhhin2.$5,000-$9,999 0.053565917 0.047206824
indhhin2.$10,000-$14,999 0.034257498 0.077752191
indhhin2.$15,000-$19,999 0.074854585 0.050752918
indhhin2.$20,000-$24,999 -0.013293014 0.082296889
indhhin2.$25,000-$34,999 -0.001623868 0.002469709
indhhin2.$45,000-$54,999 0.024608971 -0.061061909
indhhin2.$65,000-$74,999 -0.011975714 0.001674479
indhhin2.20,000+ -0.029507199 -0.005940246
indhhin2.less than $20,000 -0.011876911 0.039301796
indhhin2.$75,000-$99,999 -0.064856057 -0.069025777
bmxbmi 0.030141728 0.056879393
lbxglu 0.091058683 0.029194977
dmdmartl.Never married indhhin2.$0-$4,999
riagendr.Male -0.008466020 0.033380087
riagendr.Female 0.008466020 -0.033380087
ridageyr -0.391576503 -0.005337734
ridreth1.MexicanAmerican -0.105639997 -0.033075430
ridreth1.Other Hispanic -0.035570780 0.030129995
ridreth1.Non-Hispanic White -0.074683860 -0.034612554
ridreth1.Non-Hispanic Black 0.192682039 0.058020816
dmdeduc2.Less than 9th grade -0.095164590 0.030154308
dmdeduc2.Grades 9-11th -0.005731553 0.101146120
dmdeduc2.High school graduate/GED 0.014957400 0.014936767
dmdeduc2.Some college or AA degrees 0.051589769 -0.061416580
dmdmartl.Married -0.474254283 -0.116694334
dmdmartl.Widowed -0.133605334 0.028159045
dmdmartl.Divorced -0.162986922 0.105461562
dmdmartl.Separated -0.088564001 -0.005041179
dmdmartl.Never married 1.000000000 0.052759404
indhhin2.$0-$4,999 0.052759404 1.000000000
indhhin2.$5,000-$9,999 0.022907899 -0.039986625
indhhin2.$10,000-$14,999 0.020394547 -0.049452109
indhhin2.$15,000-$19,999 0.014224570 -0.052321316
indhhin2.$20,000-$24,999 -0.025338713 -0.053562472
indhhin2.$25,000-$34,999 0.031583171 -0.073892443
indhhin2.$45,000-$54,999 -0.023905784 -0.056882803
indhhin2.$65,000-$74,999 -0.035848411 -0.050099561
indhhin2.20,000+ 0.116319739 -0.031604563
indhhin2.less than $20,000 0.027156280 -0.026040119
indhhin2.$75,000-$99,999 0.009495700 -0.064301620
bmxbmi -0.106358679 0.040396520
lbxglu -0.127696270 -0.019100099
indhhin2.$5,000-$9,999
riagendr.Male 0.020271940
riagendr.Female -0.020271940
ridageyr 0.103081009
ridreth1.MexicanAmerican 0.061304872
ridreth1.Other Hispanic 0.011555790
ridreth1.Non-Hispanic White -0.066432877
ridreth1.Non-Hispanic Black 0.045378363
dmdeduc2.Less than 9th grade 0.143595059
dmdeduc2.Grades 9-11th 0.075501039
dmdeduc2.High school graduate/GED -0.025510668
dmdeduc2.Some college or AA degrees -0.009211246
dmdmartl.Married -0.106237151
dmdmartl.Widowed 0.138000043
dmdmartl.Divorced 0.053565917
dmdmartl.Separated 0.047206824
dmdmartl.Never married 0.022907899
indhhin2.$0-$4,999 -0.039986625
indhhin2.$5,000-$9,999 1.000000000
indhhin2.$10,000-$14,999 -0.063510172
indhhin2.$15,000-$19,999 -0.067195027
indhhin2.$20,000-$24,999 -0.068789013
indhhin2.$25,000-$34,999 -0.094898314
indhhin2.$45,000-$54,999 -0.073053236
indhhin2.$65,000-$74,999 -0.064341679
indhhin2.20,000+ -0.040588991
indhhin2.less than $20,000 -0.033442708
indhhin2.$75,000-$99,999 -0.082581047
bmxbmi 0.009554692
lbxglu -0.005718597
indhhin2.$10,000-$14,999
riagendr.Male -0.02800786
riagendr.Female 0.02800786
ridageyr 0.09951650
ridreth1.MexicanAmerican 0.06760298
ridreth1.Other Hispanic 0.06837257
ridreth1.Non-Hispanic White -0.06000445
ridreth1.Non-Hispanic Black 0.02702511
dmdeduc2.Less than 9th grade 0.12686239
dmdeduc2.Grades 9-11th 0.04053052
dmdeduc2.High school graduate/GED 0.06677701
dmdeduc2.Some college or AA degrees -0.05646402
dmdmartl.Married -0.10652619
dmdmartl.Widowed 0.11397764
dmdmartl.Divorced 0.03425750
dmdmartl.Separated 0.07775219
dmdmartl.Never married 0.02039455
indhhin2.$0-$4,999 -0.04945211
indhhin2.$5,000-$9,999 -0.06351017
indhhin2.$10,000-$14,999 1.00000000
indhhin2.$15,000-$19,999 -0.08310118
indhhin2.$20,000-$24,999 -0.08507249
indhhin2.$25,000-$34,999 -0.11736229
indhhin2.$45,000-$54,999 -0.09034612
indhhin2.$65,000-$74,999 -0.07957240
indhhin2.20,000+ -0.05019706
indhhin2.less than $20,000 -0.04135914
indhhin2.$75,000-$99,999 -0.10212932
bmxbmi -0.01186220
lbxglu 0.00247967
indhhin2.$15,000-$19,999
riagendr.Male -0.008279872
riagendr.Female 0.008279872
ridageyr 0.047583836
ridreth1.MexicanAmerican 0.051828844
ridreth1.Other Hispanic 0.043839553
ridreth1.Non-Hispanic White -0.011209740
ridreth1.Non-Hispanic Black -0.046330517
dmdeduc2.Less than 9th grade 0.070082430
dmdeduc2.Grades 9-11th 0.048786101
dmdeduc2.High school graduate/GED 0.078688206
dmdeduc2.Some college or AA degrees -0.049835260
dmdmartl.Married -0.137823165
dmdmartl.Widowed 0.026278741
dmdmartl.Divorced 0.074854585
dmdmartl.Separated 0.050752918
dmdmartl.Never married 0.014224570
indhhin2.$0-$4,999 -0.052321316
indhhin2.$5,000-$9,999 -0.067195027
indhhin2.$10,000-$14,999 -0.083101181
indhhin2.$15,000-$19,999 1.000000000
indhhin2.$20,000-$24,999 -0.090008390
indhhin2.$25,000-$34,999 -0.124171637
indhhin2.$45,000-$54,999 -0.095587998
indhhin2.$65,000-$74,999 -0.084189185
indhhin2.20,000+ -0.053109495
indhhin2.less than $20,000 -0.043758794
indhhin2.$75,000-$99,999 -0.108054857
bmxbmi -0.021396061
lbxglu 0.064868950
indhhin2.$20,000-$24,999
riagendr.Male -0.013615765
riagendr.Female 0.013615765
ridageyr 0.004340429
ridreth1.MexicanAmerican 0.080518667
ridreth1.Other Hispanic 0.010538303
ridreth1.Non-Hispanic White -0.027214942
ridreth1.Non-Hispanic Black 0.012999189
dmdeduc2.Less than 9th grade 0.093402738
dmdeduc2.Grades 9-11th 0.033182461
dmdeduc2.High school graduate/GED -0.028642427
dmdeduc2.Some college or AA degrees 0.029408267
dmdmartl.Married -0.039465404
dmdmartl.Widowed -0.002073153
dmdmartl.Divorced -0.013293014
dmdmartl.Separated 0.082296889
dmdmartl.Never married -0.025338713
indhhin2.$0-$4,999 -0.053562472
indhhin2.$5,000-$9,999 -0.068789013
indhhin2.$10,000-$14,999 -0.085072490
indhhin2.$15,000-$19,999 -0.090008390
indhhin2.$20,000-$24,999 1.000000000
indhhin2.$25,000-$34,999 -0.127117211
indhhin2.$45,000-$54,999 -0.097855517
indhhin2.$65,000-$74,999 -0.086186303
indhhin2.20,000+ -0.054369347
indhhin2.less than $20,000 -0.044796831
indhhin2.$75,000-$99,999 -0.110618112
bmxbmi 0.035630116
lbxglu 0.051934542
indhhin2.$25,000-$34,999
riagendr.Male -0.030147464
riagendr.Female 0.030147464
ridageyr -0.023087596
ridreth1.MexicanAmerican -0.022372295
ridreth1.Other Hispanic -0.031578666
ridreth1.Non-Hispanic White 0.040134962
ridreth1.Non-Hispanic Black 0.015608909
dmdeduc2.Less than 9th grade -0.040473993
dmdeduc2.Grades 9-11th 0.047684818
dmdeduc2.High school graduate/GED 0.080105990
dmdeduc2.Some college or AA degrees 0.017885975
dmdmartl.Married -0.025894374
dmdmartl.Widowed 0.040742036
dmdmartl.Divorced -0.001623868
dmdmartl.Separated 0.002469709
dmdmartl.Never married 0.031583171
indhhin2.$0-$4,999 -0.073892443
indhhin2.$5,000-$9,999 -0.094898314
indhhin2.$10,000-$14,999 -0.117362287
indhhin2.$15,000-$19,999 -0.124171637
indhhin2.$20,000-$24,999 -0.127117211
indhhin2.$25,000-$34,999 1.000000000
indhhin2.$45,000-$54,999 -0.134997191
indhhin2.$65,000-$74,999 -0.118898854
indhhin2.20,000+ -0.075005573
indhhin2.less than $20,000 -0.061799749
indhhin2.$75,000-$99,999 -0.152603909
bmxbmi -0.018310770
lbxglu 0.012113054
indhhin2.$45,000-$54,999
riagendr.Male -0.001429688
riagendr.Female 0.001429688
ridageyr -0.038052431
ridreth1.MexicanAmerican -0.021509342
ridreth1.Other Hispanic -0.012403737
ridreth1.Non-Hispanic White 0.006137821
ridreth1.Non-Hispanic Black 0.026768158
dmdeduc2.Less than 9th grade -0.035490049
dmdeduc2.Grades 9-11th -0.038028797
dmdeduc2.High school graduate/GED 0.005154086
dmdeduc2.Some college or AA degrees 0.082630518
dmdmartl.Married 0.074498358
dmdmartl.Widowed -0.034556429
dmdmartl.Divorced 0.024608971
dmdmartl.Separated -0.061061909
dmdmartl.Never married -0.023905784
indhhin2.$0-$4,999 -0.056882803
indhhin2.$5,000-$9,999 -0.073053236
indhhin2.$10,000-$14,999 -0.090346124
indhhin2.$15,000-$19,999 -0.095587998
indhhin2.$20,000-$24,999 -0.097855517
indhhin2.$25,000-$34,999 -0.134997191
indhhin2.$45,000-$54,999 1.000000000
indhhin2.$65,000-$74,999 -0.091528982
indhhin2.20,000+ -0.057739696
indhhin2.less than $20,000 -0.047573781
indhhin2.$75,000-$99,999 -0.117475316
bmxbmi 0.034408507
lbxglu 0.017172744
indhhin2.$65,000-$74,999 indhhin2.20,000+
riagendr.Male 0.006668939 -0.033416042
riagendr.Female -0.006668939 0.033416042
ridageyr 0.005053337 -0.081919744
ridreth1.MexicanAmerican -0.019822460 0.106103297
ridreth1.Other Hispanic 0.007335408 -0.015924383
ridreth1.Non-Hispanic White 0.013117724 -0.016619728
ridreth1.Non-Hispanic Black -0.002248780 -0.048907525
dmdeduc2.Less than 9th grade -0.022255450 -0.019274122
dmdeduc2.Grades 9-11th -0.015752518 0.033202359
dmdeduc2.High school graduate/GED 0.038227235 -0.012414401
dmdeduc2.Some college or AA degrees 0.015921913 -0.029218897
dmdmartl.Married 0.050051795 -0.058842981
dmdmartl.Widowed -0.042747474 -0.051180644
dmdmartl.Divorced -0.011975714 -0.029507199
dmdmartl.Separated 0.001674479 -0.005940246
dmdmartl.Never married -0.035848411 0.116319739
indhhin2.$0-$4,999 -0.050099561 -0.031604563
indhhin2.$5,000-$9,999 -0.064341679 -0.040588991
indhhin2.$10,000-$14,999 -0.079572401 -0.050197065
indhhin2.$15,000-$19,999 -0.084189185 -0.053109495
indhhin2.$20,000-$24,999 -0.086186303 -0.054369347
indhhin2.$25,000-$34,999 -0.118898854 -0.075005573
indhhin2.$45,000-$54,999 -0.091528982 -0.057739696
indhhin2.$65,000-$74,999 1.000000000 -0.050854271
indhhin2.20,000+ -0.050854271 1.000000000
indhhin2.less than $20,000 -0.041900635 -0.026432392
indhhin2.$75,000-$99,999 -0.103466452 -0.065270272
bmxbmi 0.025660367 0.025320599
lbxglu -0.064348404 0.012292850
indhhin2.less than $20,000
riagendr.Male -0.014146747
riagendr.Female 0.014146747
ridageyr 0.056240125
ridreth1.MexicanAmerican 0.004210595
ridreth1.Other Hispanic 0.008793109
ridreth1.Non-Hispanic White -0.020652926
ridreth1.Non-Hispanic Black 0.021062862
dmdeduc2.Less than 9th grade 0.058518174
dmdeduc2.Grades 9-11th -0.013741434
dmdeduc2.High school graduate/GED 0.007328464
dmdeduc2.Some college or AA degrees 0.006303120
dmdmartl.Married -0.051294066
dmdmartl.Widowed 0.027658261
dmdmartl.Divorced -0.011876911
dmdmartl.Separated 0.039301796
dmdmartl.Never married 0.027156280
indhhin2.$0-$4,999 -0.026040119
indhhin2.$5,000-$9,999 -0.033442708
indhhin2.$10,000-$14,999 -0.041359140
indhhin2.$15,000-$19,999 -0.043758794
indhhin2.$20,000-$24,999 -0.044796831
indhhin2.$25,000-$34,999 -0.061799749
indhhin2.$45,000-$54,999 -0.047573781
indhhin2.$65,000-$74,999 -0.041900635
indhhin2.20,000+ -0.026432392
indhhin2.less than $20,000 1.000000000
indhhin2.$75,000-$99,999 -0.053778489
bmxbmi 0.014670312
lbxglu 0.047248443
indhhin2.$75,000-$99,999 bmxbmi
riagendr.Male 0.037027181 -0.074050742
riagendr.Female -0.037027181 0.074050742
ridageyr -0.085864313 0.054940490
ridreth1.MexicanAmerican -0.058022583 0.116792190
ridreth1.Other Hispanic 0.021713079 0.031603671
ridreth1.Non-Hispanic White -0.030853880 -0.047290232
ridreth1.Non-Hispanic Black 0.010089490 0.106419542
dmdeduc2.Less than 9th grade -0.084235601 0.012466537
dmdeduc2.Grades 9-11th -0.025204946 0.006400369
dmdeduc2.High school graduate/GED -0.067298498 0.059502755
dmdeduc2.Some college or AA degrees 0.024863521 0.071803429
dmdmartl.Married 0.066606870 0.026017907
dmdmartl.Widowed -0.072779967 0.013266915
dmdmartl.Divorced -0.064856057 0.030141728
dmdmartl.Separated -0.069025777 0.056879393
dmdmartl.Never married 0.009495700 -0.106358679
indhhin2.$0-$4,999 -0.064301620 0.040396520
indhhin2.$5,000-$9,999 -0.082581047 0.009554692
indhhin2.$10,000-$14,999 -0.102129322 -0.011862198
indhhin2.$15,000-$19,999 -0.108054857 -0.021396061
indhhin2.$20,000-$24,999 -0.110618112 0.035630116
indhhin2.$25,000-$34,999 -0.152603909 -0.018310770
indhhin2.$45,000-$54,999 -0.117475316 0.034408507
indhhin2.$65,000-$74,999 -0.103466452 0.025660367
indhhin2.20,000+ -0.065270272 0.025320599
indhhin2.less than $20,000 -0.053778489 0.014670312
indhhin2.$75,000-$99,999 1.000000000 -0.002492344
bmxbmi -0.002492344 1.000000000
lbxglu -0.007916544 0.128391900
lbxglu
riagendr.Male 0.052538085
riagendr.Female -0.052538085
ridageyr 0.221625646
ridreth1.MexicanAmerican 0.055799978
ridreth1.Other Hispanic 0.053913405
ridreth1.Non-Hispanic White -0.048149775
ridreth1.Non-Hispanic Black 0.011610207
dmdeduc2.Less than 9th grade 0.078832553
dmdeduc2.Grades 9-11th -0.021872981
dmdeduc2.High school graduate/GED 0.038260129
dmdeduc2.Some college or AA degrees -0.040285393
dmdmartl.Married 0.027980439
dmdmartl.Widowed 0.047303877
dmdmartl.Divorced 0.091058683
dmdmartl.Separated 0.029194977
dmdmartl.Never married -0.127696270
indhhin2.$0-$4,999 -0.019100099
indhhin2.$5,000-$9,999 -0.005718597
indhhin2.$10,000-$14,999 0.002479670
indhhin2.$15,000-$19,999 0.064868950
indhhin2.$20,000-$24,999 0.051934542
indhhin2.$25,000-$34,999 0.012113054
indhhin2.$45,000-$54,999 0.017172744
indhhin2.$65,000-$74,999 -0.064348404
indhhin2.20,000+ 0.012292850
indhhin2.less than $20,000 0.047248443
indhhin2.$75,000-$99,999 -0.007916544
bmxbmi 0.128391900
lbxglu 1.000000000cor_high <- findCorrelation(cor_matrix, .9)
cor_high[1] 1high_cor_remove <- row.names(cor_matrix)[cor_high]
high_cor_remove[1] "riagendr.Male"8.5 Normalization
preProcValues.range <- preProcess(dm2_Train_independent_non_corr[,features2], method = c("range"))
dm2Train_transformed.range <- predict(preProcValues.range, dm2_Train_independent_non_corr)
dm2Test_dummies <- as_tibble( predict(dummyVars.dm2Train, dm2.test) )
dm2Test_transformed.range <- as_tibble( predict(preProcValues.range, dm2Test_dummies) )8.6 Train knn Model
install_if_not('class')[1] "the package 'class' is already installed"[1] 1126 10glimpse(dm2.test)Rows: 750
Columns: 10
$ seqn <dbl> 83734, 83737, 83757, 83761, 83789, 83820, 83822, 83823, 83834…
$ riagendr <fct> Male, Female, Female, Female, Male, Male, Female, Female, Mal…
$ ridageyr <dbl> 78, 72, 57, 24, 66, 70, 20, 29, 69, 71, 37, 49, 41, 54, 80, 3…
$ ridreth1 <fct> Non-Hispanic White, MexicanAmerican, Other Hispanic, Other, N…
$ dmdeduc2 <fct> High school graduate/GED, Grades 9-11th, Less than 9th grade,…
$ dmdmartl <fct> Married, Separated, Separated, Never married, Living with par…
$ indhhin2 <fct> "$20,000-$24,999", "$75,000-$99,999", "$20,000-$24,999", "$0-…
$ bmxbmi <dbl> 28.8, 28.6, 35.4, 25.3, 34.0, 27.0, 22.2, 29.7, 28.2, 27.6, 3…
$ diq010 <fct> Diabetes, No Diabetes, Diabetes, No Diabetes, No Diabetes, No…
$ lbxglu <dbl> 84, 107, 398, 95, 113, 94, 80, 102, 105, 76, 79, 126, 110, 99…dm2Test_transformed.range <- dm2Test_transformed.range %>%
mutate(diq010.Diabetes = as.factor(diq010.Diabetes))
glimpse(dm2Test_transformed.range)Rows: 750
Columns: 38
$ seqn <dbl> 83734, 83737, 83757, 83761, 8378…
$ riagendr.Male <dbl> 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0,…
$ riagendr.Female <dbl> 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1,…
$ ridageyr <dbl> 0.96666667, 0.86666667, 0.616666…
$ ridreth1.MexicanAmerican <dbl> 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
$ `ridreth1.Other Hispanic` <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,…
$ `ridreth1.Non-Hispanic White` <dbl> 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1,…
$ `ridreth1.Non-Hispanic Black` <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0,…
$ ridreth1.Other <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
$ `dmdeduc2.Less than 9th grade` <dbl> 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0,…
$ `dmdeduc2.Grades 9-11th` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `dmdeduc2.High school graduate/GED` <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1,…
$ `dmdeduc2.Some college or AA degrees` <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,…
$ `dmdeduc2.College grad or above` <dbl> 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0,…
$ dmdmartl.Married <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,…
$ dmdmartl.Widowed <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ dmdmartl.Divorced <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ dmdmartl.Separated <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `dmdmartl.Never married` <dbl> 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0,…
$ `dmdmartl.Living with partner` <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,…
$ `indhhin2.$0-$4,999` <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$5,000-$9,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$10,000-$14,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0,…
$ `indhhin2.$15,000-$19,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$20,000-$24,999` <dbl> 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$25,000-$34,999` <dbl> 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,…
$ `indhhin2.$35,000-$44,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$45,000-$54,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
$ `indhhin2.$55,000-$64,999` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$65,000-$74,999` <dbl> 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0,…
$ `indhhin2.20,000+` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.less than $20,000` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$75,000-$99,999` <dbl> 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ `indhhin2.$100,000+` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
$ bmxbmi <dbl> 0.25779626, 0.25363825, 0.395010…
$ diq010.Diabetes <fct> 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0,…
$ `diq010.No Diabetes` <dbl> 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1,…
$ lbxglu <dbl> 0.07925408, 0.13286713, 0.811188…dm2Test_transformed.range$knn_pred <- knn(
dm2Train_transformed.range[,features2] ,
dm2Test_transformed.range[,features2] ,
cl = dm2Train_transformed.range$diq010.Diabetes,
k = 5)
dm2Test_transformed.range$knn_prob <- (knn(
dm2Train_transformed.range[,features2] ,
dm2Test_transformed.range[,features2] ,
cl = dm2Train_transformed.range$diq010.Diabetes,
k = 5,
prob = TRUE) |>
attributes())$prob
Attaching package: 'yardstick'The following objects are masked from 'package:caret':
precision, recall, sensitivity, specificityThe following object is masked from 'package:readr':
spec Truth
Prediction 0 1
0 608 98
1 30 14# A tibble: 13 × 3
.metric .estimator .estimate
<chr> <chr> <dbl>
1 accuracy binary 0.829
2 kap binary 0.104
3 sens binary 0.953
4 spec binary 0.125
5 ppv binary 0.861
6 npv binary 0.318
7 mcc binary 0.118
8 j_index binary 0.0780
9 bal_accuracy binary 0.539
10 detection_prevalence binary 0.941
11 precision binary 0.861
12 recall binary 0.953
13 f_meas binary 0.905 dm2Test_transformed.range %>%
conf_mat(truth=diq010.Diabetes, knn_pred) %>%
summary() %>%
ggplot(aes(y=.metric, x=.estimate, fill=.metric)) +
geom_bar(stat="identity")
dm2Test_transformed.range %>%
roc_curve(truth=diq010.Diabetes, knn_prob) %>%
mutate(FPR = 1 - specificity) %>%
mutate(TPR = sensitivity) %>%
ggplot(aes(x=FPR, y=TPR)) +
geom_line() +
annotate("text",
x=.94, y=0,
label= paste("AUC: ",round(roc_auc(dm2Test_transformed.range, truth=diq010.Diabetes, knn_prob)$.estimate,3))) +
ggtitle("ROC Curve")
dm2Test_transformed.range %>%
pr_curve(truth=diq010.Diabetes, knn_prob) %>%
ggplot(aes(x=recall, y=precision)) +
geom_line() +
annotate("text",
x=.94, y=0,
label= paste("PR_AUC: ",round(pr_auc(dm2Test_transformed.range, truth=diq010.Diabetes, knn_prob)$.estimate,3))) +
ggtitle("PR Curve")
dm2Test_transformed.range %>%
gain_curve(truth=diq010.Diabetes, knn_prob) %>%
autoplot()
dm2Test_transformed.range %>%
lift_curve(truth=diq010.Diabetes, knn_prob) %>%
autoplot()