Tools for quality control, prediction and plotting of caret models
The caretExtra package includes a bunch of methods for user friendly extraction of predictions in the train, cross-validation and test data, fit statistic calulation, diagnostic via residuals plots and graphical representation of the results as scatter, ROC and heat map plots. In addition, regression models may be calibrated by the quantile GAM (generalized additive model) method.
Currently, it works only with cross-validated Caret models (CV or repreated CV) created from formula objects. Solution for bootstrap and holdout model construction methods are on the way.
Some concepts of modeling, validatin and evaluation of models followed by the caretExtra package are presented in a presentation
You may easily fetch the package and its dependencies somKernels and clustTools with devtools:
## dependencies
devtools::install_github('PiotrTymoszuk/clustTools')
devtools::install_github('PiotrTymoszuk/somKernels')
## the package
devtools::install_github('PiotrTymoszuk/caretExtra')
The package is available under a GPL-3 license.
The package maintainer is Piotr Tymoszuk.
caretExtra uses tools provided by the rlang, tidyverse, caret, coxed, ggrepel, generics, DescTools, plotROC, qgam, and survial. Many thanks to their developers, maintainers and contributors.
In the example of basic usage of the caretExtra tools, I'll use three published data sets:
-
Bostonprovided by the R packageMASSconsisting of data on environmental, geographic and socioeconomic features of houses in Boston as well as their value. This data set will be used for modeling of the house value with regression -
biopsyincluded in theMASSpackage consisting of nine pathological and cytologic properties of breast lesion biopsies rated by the pathologist with a 1 - 10 point scale. With this data set I'll construct a binary classifier predicting the biopsy sample classification as benign or malignant tissue -
winesincluded in thekohonenpackage. and consisting of physical and chemical properties of various Italian wines. This data set will be used to model vintage class with a multi-category classifier
For each data set, randomly selected two-thirds of observations will be used for tuning of the models with 5-repeats 5 fold cross-validation. The remaining observations will be put aside for the final evaluation of the model performance.
Let's start with few packages which are required for the current analysis:
## the 'biopsy' and `Boston` data sets provided by MASS
library(MASS)
## the 'wines' data set provided by kohonen
library(kohonen)
## packages used for analysis
library(tidyverse)
library(caret)
library(caretExtra)
library(gbm)
## doParallel for a parallel backend
## used for tuning and cross-validation
library(doParallel)
## patchwork for plot panels
library(patchwork)Preprocessing is in many cases a tedious step preceding the 'real' analysis. For the Boston data set, this includes recoding of the dummy chas variable and normalization of numeric explanatory features as well as selection of the training and test subset observations with the createDataPartition() function from the caret package.
## Boston: used for the regression model of the house price
## normalization of numeric explanatory variables
## adding the observation ID in the rownames
my_boston <- Boston %>%
filter(complete.cases(.)) %>%
mutate(chas = car::recode(chas,
"0 = 'no';
1 = 'yes'"),
chas = factor(chas, c('no', 'yes')))
my_boston[, c("crim", "zn", "indus",
"nox", "rm", "age",
"dis", "tax", "rad",
"ptratio", "black", "lstat")] <-
my_boston[, c("crim", "zn", "indus",
"nox", "rm", "age",
"dis", "tax", "rad",
"ptratio", "black", "lstat")] %>%
map_dfc(~scale(.x)[, 1])
rownames(my_boston) <- paste0('obs_', 1:nrow(my_boston))
## training and test portion at a 2 to 1 ratio
boston_ids <- createDataPartition(my_boston$medv, p = 2/3)[[1]]
my_boston <- list(train = my_boston[boston_ids, ],
test = my_boston[-boston_ids, ])> my_boston %>% map(head)
$train
crim zn indus chas nox rm age dis rad tax ptratio
obs_2 -0.4169267 -0.48724019 -0.5927944 no -0.7395304 0.1940824 0.36680343 0.5566090 -0.8670245 -0.9863534 -0.3027945
obs_6 -0.4166314 -0.48724019 -1.3055857 no -0.8344581 0.2068916 -0.35080997 1.0766711 -0.7521778 -1.1050216 0.1129203
obs_7 -0.4098372 0.04872402 -0.4761823 no -0.2648919 -0.3880270 -0.07015919 0.8384142 -0.5224844 -0.5769480 -1.5037485
obs_8 -0.4032966 0.04872402 -0.4761823 no -0.2648919 -0.1603069 0.97784057 1.0236249 -0.5224844 -0.5769480 -1.5037485
obs_10 -0.4003331 0.04872402 -0.4761823 no -0.2648919 -0.3994130 0.61548134 1.3283202 -0.5224844 -0.5769480 -1.5037485
obs_11 -0.3939564 0.04872402 -0.4761823 no -0.2648919 0.1314594 0.91389483 1.2117800 -0.5224844 -0.5769480 -1.5037485
black lstat medv
obs_2 0.4406159 -0.49195252 21.6
obs_6 0.4101651 -1.04229087 28.7
obs_7 0.4263763 -0.03123671 22.9
obs_8 0.4406159 0.90979986 27.1
obs_10 0.3289995 0.62272769 18.9
obs_11 0.3926395 1.09184562 15.0
$test
crim zn indus chas nox rm age dis rad tax ptratio black
obs_1 -0.4193669 0.28454827 -1.2866362 no -0.1440749 0.4132629 -0.1198948 0.140075 -0.9818712 -0.6659492 -1.4575580 0.4406159
obs_3 -0.4169290 -0.48724019 -0.5927944 no -0.7395304 1.2814456 -0.2655490 0.556609 -0.8670245 -0.9863534 -0.3027945 0.3960351
obs_4 -0.4163384 -0.48724019 -1.3055857 no -0.8344581 1.0152978 -0.8090878 1.076671 -0.7521778 -1.1050216 0.1129203 0.4157514
obs_5 -0.4120741 -0.48724019 -1.3055857 no -0.8344581 1.2273620 -0.5106743 1.076671 -0.7521778 -1.1050216 0.1129203 0.4406159
obs_9 -0.3955433 0.04872402 -0.4761823 no -0.2648919 -0.9302853 1.1163897 1.086122 -0.5224844 -0.5769480 -1.5037485 0.3281233
obs_12 -0.4064448 0.04872402 -0.4761823 no -0.2648919 -0.3922967 0.5089051 1.154792 -0.5224844 -0.5769480 -1.5037485 0.4406159
lstat medv
obs_1 -1.07449897 24.0
obs_3 -1.20753241 34.7
obs_4 -1.36017078 33.4
obs_5 -1.02548665 36.2
obs_9 2.41937935 16.5
obs_12 0.08639286 18.9Pre-processing of the biopsy data set is quite easy: I'm not going to normalize the numeric explanatory variables because they are anyway on the same scale. Still, I'm setting unique observation names and selecting the training and test observations with createDataPartition():
## biopsy: binary classification of benign and malignant samples
## explanatory variables are not normalized, since they
## are anyway in the same 1 - 10 scale
## IDs in the rownames
my_biopsy <- biopsy %>%
filter(complete.cases(.))
rownames(my_biopsy) <-
paste(my_biopsy$ID, 1:nrow(my_biopsy), sep = '_sample_')
my_biopsy <- my_biopsy %>%
select(-ID)
## training and test portion at a 2 to 1 ratio
biopsy_ids <- createDataPartition(my_biopsy$class, p = 2/3)[[1]]
my_biopsy <- list(train = my_biopsy[biopsy_ids, ],
test = my_biopsy[-biopsy_ids, ])> my_biopsy %>% map(head)
$train
V1 V2 V3 V4 V5 V6 V7 V8 V9 class
1000025_sample_1 5 1 1 1 2 1 3 1 1 benign
1002945_sample_2 5 4 4 5 7 10 3 2 1 benign
1016277_sample_4 6 8 8 1 3 4 3 7 1 benign
1017023_sample_5 4 1 1 3 2 1 3 1 1 benign
1035283_sample_11 1 1 1 1 1 1 3 1 1 benign
1036172_sample_12 2 1 1 1 2 1 2 1 1 benign
$test
V1 V2 V3 V4 V5 V6 V7 V8 V9 class
1015425_sample_3 3 1 1 1 2 2 3 1 1 benign
1017122_sample_6 8 10 10 8 7 10 9 7 1 malignant
1018099_sample_7 1 1 1 1 2 10 3 1 1 benign
1018561_sample_8 2 1 2 1 2 1 3 1 1 benign
1033078_sample_9 2 1 1 1 2 1 1 1 5 benign
1033078_sample_10 4 2 1 1 2 1 2 1 1 benignIn the wines data set I need to rename variables to be compatible with R formulas, normalize explanatory variables and select the training and test observations in a similar way as for the remaining data sets:
## wines: multi-level classification of vintages
## normalization of explanatory variables
## IDs in the rownames
data(wines)
my_wines <- wines %>%
as.data.frame %>%
filter(complete.cases(.)) %>%
map_dfc(~scale(.x)[, 1])
names(my_wines) <- make.names(names(my_wines))
my_wines$vintage <- vintages
my_wines <- as.data.frame(my_wines)
rownames(my_wines) <- paste0('wine_', 1:nrow(my_wines))
## training - test split at a 2 to 1 ratio
wines_ids <- createDataPartition(my_wines$vintage, p = 2/3)[[1]]
my_wines <- list(train = my_wines[wines_ids, ],
test = my_wines[-wines_ids, ])
> my_wines %>% map(head)
$train
alcohol malic.acid ash ash.alkalinity magnesium tot..phenols flavonoids non.flav..phenols proanth
wine_1 0.2551008 -0.50020530 -0.8221529 -2.4930372 0.02909756 0.5710456 0.7375437 -0.8208101 -0.5370519
wine_2 0.2056453 0.01796903 1.1045562 -0.2748590 0.09964918 0.8104843 1.2181890 -0.4999191 2.1399040
wine_3 1.7016732 -0.34832662 0.4865552 -0.8144158 0.94626865 2.4865554 1.4685250 -0.9812556 1.0376281
wine_6 1.7264010 -0.41979894 0.3047901 -1.4738742 -0.25310893 0.3316069 0.4972211 -0.4999191 0.6876992
wine_7 1.3183934 -0.16964581 0.8864382 -0.5746128 1.51068164 0.4912327 0.4872077 -0.4196964 -0.5895412
wine_8 2.2704111 -0.62528187 -0.7130939 -1.6537265 -0.18255731 0.8104843 0.9578395 -0.5801419 0.6876992
col..int. col..hue OD.ratio proline vintage
wine_1 -0.290306650 0.4059482 1.1284966 0.9683055 Barolo
wine_2 0.268966296 0.3186634 0.8023031 1.3970348 Barolo
wine_3 1.181011408 -0.4232572 1.1994082 2.3338876 Barolo
wine_6 0.083976014 0.2750210 1.3837785 1.7304908 Barolo
wine_7 -0.002065978 0.4495905 1.3837785 1.7463697 Barolo
wine_8 0.062465516 0.5368753 0.3484686 0.9524266 Barolo
$test
alcohol malic.acid ash ash.alkalinity magnesium tot..phenols flavonoids non.flav..phenols proanth col..int.
wine_4 0.3045563 0.2234520 1.8316163 0.4445501 1.2990268 0.8104843 0.6674496 0.2220856 0.40775610 -0.31611925
wine_5 1.4914875 -0.5180734 0.3047901 -1.2940219 0.8757170 1.5607256 1.3683906 -0.1790282 0.67020276 0.72929095
wine_11 1.3925766 -0.7682265 -0.1677989 -0.8144158 -0.3236606 -0.1472706 0.4071002 -0.8208101 -0.02965499 -0.02357648
wine_16 1.6151262 -0.3751287 1.2863212 0.1447963 1.4401300 0.8104843 1.1180545 -0.2592509 0.67020276 0.49267547
wine_24 0.6260168 -0.4734032 0.8864382 0.1447963 -0.2531089 0.3794946 0.5873421 -0.6603646 0.12781300 -0.66028721
wine_26 0.4900143 -0.5091393 0.9227912 -1.0242435 -0.4647638 0.8902972 0.9177857 -0.1790282 -0.23961231 -0.10961847
col..hue OD.ratio proline vintage
wine_4 0.3623058 0.46192721 -0.03206274 Barolo
wine_5 0.4059482 0.34846859 2.23861439 Barolo
wine_11 0.9296568 0.30592161 1.69873311 Barolo
wine_16 0.4932329 0.06482205 1.69873311 Barolo
wine_24 0.7114449 1.72415433 0.31727220 Barolo
wine_26 -0.1614029 0.87321470 1.42879247 Barolocaret models to be analyzed with the caretExtra tools need to meet few requirements. They need to be constructed with a formula, tuned in a cross-validation setting (single or repeated), contain the training data, final predictions and performance metrics in final resamples. Specifically for classification models, propabilities of class assignment need to be included in the caret object. To make sure that all those elements are included in modeling results, we customize the trainControl object as presented below:
train_control <- trainControl(method = 'repeatedcv',
number = 5,
repeats = 5,
savePredictions = 'final',
returnData = TRUE,
returnResamp = 'final',
classProbs = TRUE)Setting savePredictions = 'final', returnData = TRUE, returnResamp = 'final' and, for classification models, classProbs = TRUE is absolutely crucial for subsequent analysis. Calling as_caretx() for caret models without these components raises an error.
Construction of caret models is done as usual with the train() function. Importantly, the models must be built with formulas and not with the x and y variable matrices, and the customized trainControl object needs to be passed to the trControl argument. In the current example, I will fit the requested models with the gradient boosted machine (GBM) algorithm and default tune grids. You are however welcome to test a richer set of combinations of the tuning parameters.
my_models <- list()
registerDoParallel(cores = 7)
set.seed(12345)
my_models$regression <- caret::train(form = medv ~ .,
data = my_boston$train,
metric = 'MAE',
method = 'gbm',
trControl = train_control)
my_models$binary <- caret::train(form = class ~ .,
data = my_biopsy$train,
metric = 'Kappa',
method = 'gbm',
trControl = train_control)
my_models$multi_class <- caret::train(form = vintage ~ .,
data = my_wines$train,
metric = 'Kappa',
method = 'gbm',
trControl = train_control)
stopImplicitCluster()
The final step of the model construction is simple: I'm just calling as_caretx() for the models. Of importance, the function returns objects of the caretx class, which inherit most of the methods from traditional caret models.
my_models <- my_models %>%
map(as_caretx)
My personal experience with the anyway excellent caret package was that regression and classification models required different and quite often project-specific approaches to diagnostic, performance evaluation and visualization. This was my prime motivation to develop the caretExtra package. Another motivation was to create a framework compatible with tidyverse environment and ggplot graphic interface. For this reasons, the package offers a relatively simple S3 method interface (model.frame(), plot(), summary(), components() etc.), returns numeric statistics in tibble form and generates ggplot graphical objects that can be easily customized by the user.
For instance, model.frame() and formula() extract respectively the training data and the formula from the model.
> head(model.frame(my_models$regression))
crim zn indus chas nox rm age dis rad tax ptratio
obs_2 -0.4169267 -0.48724019 -0.5927944 no -0.7395304 0.1940824 0.36680343 0.5566090 -0.8670245 -0.9863534 -0.3027945
obs_6 -0.4166314 -0.48724019 -1.3055857 no -0.8344581 0.2068916 -0.35080997 1.0766711 -0.7521778 -1.1050216 0.1129203
obs_7 -0.4098372 0.04872402 -0.4761823 no -0.2648919 -0.3880270 -0.07015919 0.8384142 -0.5224844 -0.5769480 -1.5037485
obs_8 -0.4032966 0.04872402 -0.4761823 no -0.2648919 -0.1603069 0.97784057 1.0236249 -0.5224844 -0.5769480 -1.5037485
obs_10 -0.4003331 0.04872402 -0.4761823 no -0.2648919 -0.3994130 0.61548134 1.3283202 -0.5224844 -0.5769480 -1.5037485
obs_11 -0.3939564 0.04872402 -0.4761823 no -0.2648919 0.1314594 0.91389483 1.2117800 -0.5224844 -0.5769480 -1.5037485
black lstat medv
obs_2 0.4406159 -0.49195252 21.6
obs_6 0.4101651 -1.04229087 28.7
obs_7 0.4263763 -0.03123671 22.9
obs_8 0.4406159 0.90979986 27.1
obs_10 0.3289995 0.62272769 18.9
obs_11 0.3926395 1.09184562 15.0
> formula(my_models$binary)
class ~ V1 + V2 + V3 + V4 + V5 + V6 + V7 + V8 + V9
attr(,"variables")
list(class, V1, V2, V3, V4, V5, V6, V7, V8, V9)
attr(,"factors")
V1 V2 V3 V4 V5 V6 V7 V8 V9
class 0 0 0 0 0 0 0 0 0
V1 1 0 0 0 0 0 0 0 0
V2 0 1 0 0 0 0 0 0 0
V3 0 0 1 0 0 0 0 0 0
V4 0 0 0 1 0 0 0 0 0
V5 0 0 0 0 1 0 0 0 0
V6 0 0 0 0 0 1 0 0 0
V7 0 0 0 0 0 0 1 0 0
V8 0 0 0 0 0 0 0 1 0
V9 0 0 0 0 0 0 0 0 1
attr(,"term.labels")
[1] "V1" "V2" "V3" "V4" "V5" "V6" "V7" "V8" "V9"
attr(,"order")
[1] 1 1 1 1 1 1 1 1 1
attr(,"intercept")
[1] 1
attr(,"response")
[1] 1
attr(,"predvars")
list(class, V1, V2, V3, V4, V5, V6, V7, V8, V9)
attr(,"dataClasses")
class V1 V2 V3 V4 V5 V6 V7 V8 V9
"factor" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric" "numeric"
Model residuals are computed by residuals(), while augment() returns the actual outcome (.outcome) with predictions in the training data and out-of-fold predictions in resamples (.fitted) together with class assignment probabilities and the explanatory variables. By providing the residuals() and augment() functions with a test data set passed to the optional newdata argument, test set residuals and predictions can be calculated as well:
> residuals(my_models$regression, newdata = my_boston$test)
$train
# A tibble: 338 × 8
.observation .outcome .fitted .resid .std.resid .sq.std.resid .candidate_missfit .expect.norm
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 246 50 34.3 -15.7 -7.66 58.6 yes -2.97
2 4 27.1 20.6 -6.52 -3.18 10.1 yes -2.62
3 244 50 43.7 -6.25 -3.05 9.31 yes -2.44
4 111 36.2 30.1 -6.11 -2.98 8.88 yes -2.31
5 259 23.2 17.2 -5.96 -2.91 8.47 yes -2.22
6 277 17.9 13.3 -4.57 -2.23 4.97 yes -2.14
7 171 50 45.4 -4.56 -2.23 4.95 yes -2.07
8 131 23.7 19.2 -4.49 -2.19 4.79 yes -2.01
9 122 50 45.5 -4.46 -2.17 4.73 yes -1.96
10 58 28.4 24.9 -3.45 -1.69 2.84 no -1.91
# … with 328 more rows
# ℹ Use `print(n = ...)` to see more rows
$cv
# A tibble: 1,690 × 9
.observation .outcome .fitted .resample .resid .std.resid .sq.std.resid .candidate_missfit .expect.norm
<int> <dbl> <dbl> <chr> <dbl> <dbl> <dbl> <chr> <dbl>
1 246 50 23.5 Fold5.Rep3 -26.5 -7.52 56.6 yes -3.44
2 246 50 24.9 Fold1.Rep5 -25.1 -7.12 50.7 yes -3.13
3 246 50 25.5 Fold5.Rep4 -24.5 -6.94 48.2 yes -2.97
4 246 50 25.7 Fold4.Rep2 -24.3 -6.88 47.4 yes -2.87
5 246 50 27.1 Fold4.Rep1 -22.9 -6.49 42.2 yes -2.79
6 244 50 31.2 Fold2.Rep4 -18.8 -5.34 28.5 yes -2.72
7 244 50 32.6 Fold3.Rep1 -17.4 -4.92 24.2 yes -2.67
8 245 50 34.1 Fold2.Rep4 -15.9 -4.49 20.2 yes -2.62
9 245 50 34.6 Fold3.Rep1 -15.4 -4.36 19.0 yes -2.57
10 245 50 36.8 Fold5.Rep3 -13.2 -3.73 13.9 yes -2.54
# … with 1,680 more rows
# ℹ Use `print(n = ...)` to see more rows
$test
# A tibble: 168 × 8
.observation .outcome .fitted .resid .std.resid .sq.std.resid .candidate_missfit .expect.norm
<int> <dbl> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
1 127 50 30.4 -19.6 -5.13 26.4 yes -2.75
2 74 37 30.2 -6.77 -1.75 3.06 no -2.37
3 86 30.1 23.4 -6.72 -1.73 3.01 no -2.17
4 87 50 43.7 -6.28 -1.62 2.62 no -2.04
5 73 32 26.4 -5.55 -1.43 2.03 no -1.93
6 160 19.1 14.3 -4.82 -1.23 1.52 no -1.84
7 77 34.9 30.1 -4.77 -1.22 1.49 no -1.77
8 100 29.1 24.4 -4.70 -1.20 1.44 no -1.70
9 76 34.6 29.9 -4.66 -1.19 1.42 no -1.64
10 81 50 45.6 -4.43 -1.13 1.28 no -1.58
# … with 158 more rows
# ℹ Use `print(n = ...)` to see more rows
> augment(my_models$multi_class)
$train
# A tibble: 119 × 19
.observ…¹ .outc…² .fitted Barbera Barolo Grign…³ alcohol malic…⁴ ash ash.a…⁵ magne…⁶ tot..…⁷ flavo…⁸ non.f…⁹ proanth col..i…˟
<int> <fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Barolo Barolo 1.40e-7 1.00 4.03e-6 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537 -0.290
2 2 Barolo Barolo 5.79e-6 1.00 1.95e-6 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14 0.269
3 3 Barolo Barolo 3.83e-6 1.00 6.18e-6 1.70 -0.348 0.487 -0.814 0.946 2.49 1.47 -0.981 1.04 1.18
4 4 Barolo Barolo 6.27e-7 1.00 1.97e-6 1.73 -0.420 0.305 -1.47 -0.253 0.332 0.497 -0.500 0.688 0.0840
5 5 Barolo Barolo 1.37e-6 1.00 1.16e-6 1.32 -0.170 0.886 -0.575 1.51 0.491 0.487 -0.420 -0.590 -0.00207
6 6 Barolo Barolo 4.92e-7 1.00 8.38e-6 2.27 -0.625 -0.713 -1.65 -0.183 0.810 0.958 -0.580 0.688 0.0625
7 7 Barolo Barolo 9.03e-7 1.00 7.42e-5 1.07 -0.884 -0.350 -1.05 -0.112 1.10 1.13 -1.14 0.460 0.931
8 8 Barolo Barolo 7.73e-6 1.00 5.34e-5 1.37 -0.161 -0.241 -0.455 0.382 1.05 1.30 -1.14 1.39 0.299
9 9 Barolo Barolo 5.56e-7 1.00 2.52e-6 0.935 -0.545 0.159 -1.05 -0.747 0.491 0.738 -0.580 0.390 0.235
10 10 Barolo Barolo 8.32e-6 1.00 2.83e-4 2.17 -0.545 0.0867 -2.43 -0.606 1.29 1.67 0.543 2.14 0.149
# … with 109 more rows, 3 more variables: col..hue <dbl>, OD.ratio <dbl>, proline <dbl>, and abbreviated variable names
# ¹.observation, ².outcome, ³Grignolino, ⁴malic.acid, ⁵ash.alkalinity, ⁶magnesium, ⁷tot..phenols, ⁸flavonoids,
# ⁹non.flav..phenols, ˟col..int.
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
$cv
# A tibble: 595 × 20
.observa…¹ .outc…² .fitted .resa…³ Barbera Barolo Grign…⁴ alcohol malic…⁵ ash ash.a…⁶ magne…⁷ tot..…⁸ flavo…⁹ non.f…˟ proanth
<int> <fct> <fct> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 1 Barolo Barolo Fold5.… 4.01e- 7 1.00 1.77e-5 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537
2 1 Barolo Barolo Fold3.… 2.07e- 7 1.00 1.42e-6 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537
3 1 Barolo Barolo Fold5.… 7.50e-10 1.00 1.86e-7 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537
4 1 Barolo Barolo Fold4.… 1.01e- 7 1.00 3.10e-5 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537
5 1 Barolo Barolo Fold3.… 2.77e- 9 1.00 1.05e-6 0.255 -0.500 -0.822 -2.49 0.0291 0.571 0.738 -0.821 -0.537
6 2 Barolo Barolo Fold1.… 2.27e- 6 1.00 1.12e-6 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14
7 2 Barolo Barolo Fold1.… 1.25e- 6 1.00 1.64e-6 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14
8 2 Barolo Barolo Fold2.… 5.31e- 7 1.00 3.59e-7 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14
9 2 Barolo Barolo Fold3.… 2.19e- 5 1.00 8.17e-6 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14
10 2 Barolo Barolo Fold5.… 2.74e- 6 1.00 2.02e-6 0.206 0.0180 1.10 -0.275 0.0996 0.810 1.22 -0.500 2.14
# … with 585 more rows, 4 more variables: col..int. <dbl>, col..hue <dbl>, OD.ratio <dbl>, proline <dbl>, and abbreviated variable
# names ¹.observation, ².outcome, ³.resample, ⁴Grignolino, ⁵malic.acid, ⁶ash.alkalinity, ⁷magnesium, ⁸tot..phenols, ⁹flavonoids,
# ˟non.flav..phenols
# ℹ Use `print(n = ...)` to see more rows, and `colnames()` to see all variable names
Many other elements of the model like tuning results, square distances to the outcome and confusion matrices can be extracted from the model with the components() method. As described above, if newdata is specified, the requested metrics and objects are generated not only for the training and cross-validaiton data sets but also for the test data:
> components(my_models$multi_class, what = 'tuning')
shrinkage interaction.depth n.minobsinnode n.trees Accuracy Kappa AccuracySD KappaSD
1 0.1 1 10 50 0.9549478 0.9318877 0.03798866 0.05703971
4 0.1 2 10 50 0.9648870 0.9468514 0.03080134 0.04637411
7 0.1 3 10 50 0.9598021 0.9389724 0.02532945 0.03850012
2 0.1 1 10 100 0.9599478 0.9394119 0.03921166 0.05894816
5 0.1 2 10 100 0.9664870 0.9492163 0.02368318 0.03580442
8 0.1 3 10 100 0.9596029 0.9386257 0.03318834 0.05055427
3 0.1 1 10 150 0.9682986 0.9519018 0.03001619 0.04548033
6 0.1 2 10 150 0.9664870 0.9490494 0.03156632 0.04809489
9 0.1 3 10 150 0.9613420 0.9414862 0.03433323 0.05161714
> components(my_models$binary, what = 'square_dist')
$train
# A tibble: 456 × 4
.observation .outcome .fitted square_dist
<int> <fct> <fct> <dbl>
1 1 benign benign 0.00000120
2 2 benign benign 0.0519
3 3 benign benign 0.0759
4 4 benign benign 0.00000704
5 5 benign benign 0.0000145
6 6 benign benign 0.000000831
7 7 malignant malignant 0.0232
8 8 benign benign 0.000129
9 9 malignant malignant 0.000000131
10 10 benign benign 0.00000120
# … with 446 more rows
# ℹ Use `print(n = ...)` to see more rows
$cv
# A tibble: 2,280 × 5
.observation .resample .outcome .fitted square_dist
<int> <chr> <fct> <fct> <dbl>
1 1 Fold1.Rep3 benign benign 0.000000516
2 1 Fold4.Rep1 benign benign 0.000000222
3 1 Fold3.Rep2 benign benign 0.000000922
4 1 Fold5.Rep4 benign benign 0.000000351
5 1 Fold2.Rep5 benign benign 0.000000645
6 2 Fold1.Rep4 benign malignant 0.980
7 2 Fold4.Rep1 benign malignant 0.981
8 2 Fold5.Rep2 benign malignant 0.946
9 2 Fold3.Rep3 benign malignant 0.981
10 2 Fold5.Rep5 benign malignant 0.984
# … with 2,270 more rows
# ℹ Use `print(n = ...)` to see more rows> components(my_models$binary,
+ what = 'confusion',
+ newdata = my_biopsy$test)
$train
.fitted
.outcome benign malignant
benign 295 1
malignant 1 159
$cv
.fitted
.outcome benign malignant
benign 1440 40
malignant 30 770
$test
.fitted
.outcome benign malignant
benign 144 4
malignant 5 74Regression models
By calling plot(type = 'diagnostic') for a regression model, a list of ggplot objects with diagnostic plots of residuals is returned. Such list includes plots of residuals versus fitted (resid_fitted), standardized residuals versus fitted (std.resid_fitted), square residuals versus fitted (sq.resid_fitted) and quantile-quantile plot of residuals, which can be helpful at assessment of residuals' normality (qq.std.resid). Candidate outliers identified with the newdata is specified, diagnostic plots will be generated for the test data set as well.
regression_resid_plots <- plot(my_models$regression,
newdata = my_boston$test,
type = 'diagnostic')
regression_resid_fitted_plots <- regression_resid_plots %>%
map(~.x$resid_fitted) %>%
map2(., c('Boston: training', 'Boston: CV', 'Boston: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom'))
regression_qqplots <- regression_resid_plots %>%
map(~.x$qq.std.resid) %>%
map2(., c('Boston: training', 'Boston: CV', 'Boston: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom')) regression_resid_fitted_plots$train +
regression_resid_fitted_plots$cv +
regression_resid_fitted_plots$test +
plot_layout(ncol = 2)
regression_qqplots$train +
regression_qqplots$cv +
regression_qqplots$test +
plot_layout(ncol = 2)
In many instances plots of predicted and observed values of the outcome variable are useful, e.g. for assessing model calibration. They are generated by calling plot(type = 'fit'). In such plots, the ideal calibration is represented by a slope 1 dashed line. By default, LOESS or GAM trends (geom_smooth() from ggplot2) are displayed as well; R^2 and RMSE values are displayed in the plot subtitle and numbers of complete observations are indicated in the plot tag:
regression_fit_predict <- plot(my_models$regression,
newdata = my_boston$test,
type = 'fit')
regression_fit_predict <- regression_fit_predict %>%
map2(., c('Boston: training', 'Boston: CV', 'Boston: test'),
~.x +
labs(title = .y) +
theme(plot.tag.position = 'bottom'))
regression_fit_predict$train +
regression_fit_predict$cv +
regression_fit_predict$test +
plot_layout(ncol = 2)
A graphical representation of the key performance statistics: R^2, RMSE and Spearman's plot(type = 'performance'):
regression_performance_plots <- plot(my_models$regression,
newdata = my_boston$test,
type = 'performance')
regression_performance_plots +
scale_size_area(limits = c(0, 1))
Binary classifiers
Class assignment probability and square distance to the outcome may serve as quality measures for classification models. They can be obtained by calling plot(type = 'class_p'). In this case, a list of scatter plots of sorted class assignment probabilities (winner_p) and squared distance to the outcome (square_dist) is returned. Misclassified observations are highlighted in red. By default, overall accuracy, Cohen's
binary_p_plots <- plot(my_models$binary,
newdata = my_biopsy$test,
type = 'class_p')
binary_sq_dist_plots <- binary_p_plots %>%
map(~.x$square_dist) %>%
map2(c('Biopsy: training', 'Biopsy: CV', 'Biopsy: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom'))
binary_p_plots <- binary_p_plots %>%
map(~.x$winner_p) %>%
map2(c('Biopsy: training', 'Biopsy: CV', 'Biopsy: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom')) binary_sq_dist_plots$train +
binary_sq_dist_plots$cv +
binary_sq_dist_plots$test +
plot_layout(ncol = 2)
binary_p_plots$train +
binary_p_plots$cv +
binary_p_plots$test +
plot_layout(ncol = 2)
Heat map representations of confusion matrices are fetched by plot(type = 'confusion'). By default, such heat maps of confusion matrices visualize observation counts. To make them show percentages, the user can specify scale = 'percent':
binary_confusion_plots <- plot(my_models$binary,
newdata = my_biopsy$test,
type = 'confusion',
scale = 'percent')
binary_confusion_plots <- binary_confusion_plots %>%
map2(c('Biopsy: training', 'Biopsy: CV', 'Biopsy: test'),
~.x +
labs(title = .y) +
theme(plot.tag.position = 'bottom',
legend.position = 'none'))
binary_confusion_plots$train +
binary_confusion_plots$cv +
binary_confusion_plots$test +
plot_layout(ncol = 2)
Graphical representation of results of receiver operating characteristic, so called 'ROC curves', is a traditional way to visualize sensitivity, specificity and overall accuracy (so called area under the curve or AUC) of a classifier. Such plots are obtained with plot(type = 'roc'). Again, by specifying newdata, ROC curves will be plotted also for the test data set. Sensitivity (Se), specificity (Sp) at the p = 0.5 class assignment probability cutoff as well as AUC are presented in the plots.
binary_roc_plots <- plot(my_models$binary,
newdata = my_biopsy$test,
type = 'roc')
binary_roc_plots <- binary_roc_plots %>%
map2(c('Biopsy: training', 'Biopsy: CV', 'Biopsy: test'),
~.x +
labs(title = .y) +
theme(plot.tag.position = 'bottom'))
binary_roc_plots$train +
binary_roc_plots$cv +
binary_roc_plots$test +
plot_layout(ncol = 2)
By calling plot(type = 'performance'), the most essential performance statistics, overall accuracy, Cohen's ggplot2 object returned by the package's tools, feel free to modify it!
binary_performance_plots <- plot(my_models$binary,
newdata = my_biopsy$test,
type = 'performance')
## indicating kappa and Brier score values expected
## for a dummy classifier
binary_performance_plots +
geom_hline(yintercept = 0.75, linetype = 'dashed') +
geom_vline(xintercept = 0, linetype = 'dashed') +
scale_size_area(limits = c(0.5, 1))
Multi-category classifiers
In general, the repertoire of plots for multi-category classifiers is similar to binary classifer plots with exception of ROC curves which are available only for the later.
## plots of squared distances
## and class assignment probabilities
multi_p_plots <- plot(my_models$multi_class,
newdata = my_wines$test,
type = 'class_p')
multi_p_sq_dist_plots <- multi_p_plots %>%
map(~.x$square_dist) %>%
map2(., c('Wines: training', 'Wines: CV', 'Wines: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom'))
multi_p_sq_dist_plots$train +
multi_p_sq_dist_plots$cv +
multi_p_sq_dist_plots$test +
plot_layout(ncol = 2)
## confusion matrix plots
multi_confusion_plots <- plot(my_models$multi_class,
newdata = my_wines$test,
type = 'confusion')
multi_confusion_plots <- multi_confusion_plots %>%
map2(., c('Wines: training', 'Wines: CV', 'Wines: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'none',
plot.tag.position = 'bottom'))
multi_confusion_plots$train +
multi_confusion_plots$cv +
multi_confusion_plots$test +
plot_layout(ncol = 2)
The most crucial model performance statistics can be computed for caretex models with the summary() method:
-
regression models: mean absolute error (
MAE), mean square error (MSE), root mean square error (RMSE), pseudo-R^2 metric of explained variance (rsq), R^2 computed as square Pearson's correlation coefficient (caret_rsq) as well as coefficients of correlation between the outcome and predictions (Pearson's r:pearson, Spearman's$\rho$ :spearman, Kendall's$\tau B$ :kendall) -
binary classifiers: Harrel's concordance index (
c_index), log loss (log_loss), area under the ROC curve (AUC), area under the precision-recall curve (prAUC), overall accuracy (correct_rate), Cohen's$\kappa$ (kappa), F1 score (F1), sensitivity and specificity (SeandSp), positive and negative prdiction value (PPVandNPV), precision, recall, detection rate (detection_rate), balanced accuaracy (balanced_accuracy), Brier score (brier_score), as well as mean assignment probabilty in the outcome and fitted classes (class_p_outcomeandclass_p_fitted) -
multi-category classifiers: as above except of Harrel's concordance index. Note that ROC metrics are averaged over all classes. Additionally, Brier score is calculated in a bit different way for multi-category classification models as compared with binary classifiers (see: the note by Goldstein-Greenwood in the reference list)
The function computes the performance statistics for predictions in the training data set and out-of-fold predictions. By specifying the optional newdata argument, evaluation of model performance in the test data set will be done as well.
regression_stats <- summary(my_models$regression,
newdata = my_boston$test)
binary_stats <- summary(my_models$binary,
newdata = my_biopsy$test)
multi_class_stats <- summary(my_models$multi_class,
newdata = my_wines$test)> regression_stats
$train
# A tibble: 8 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 MAE 1.46 NA NA
2 MSE 4.19 NA NA
3 RMSE 2.05 NA NA
4 rsq 0.949 NA NA
5 caret_rsq 0.950 NA NA
6 pearson 0.975 NA NA
7 spearman 0.962 NA NA
8 kendall 0.849 NA NA
$cv
# A tibble: 8 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <dbl> <dbl>
1 MAE 2.44 1.97 3.11
2 MSE 12.4 6.32 22.5
3 RMSE 3.45 2.51 4.74
4 rsq 0.850 0.700 0.916
5 caret_rsq 0.855 0.716 0.917
6 pearson 0.924 0.846 0.958
7 spearman 0.908 0.857 0.956
8 kendall 0.762 0.701 0.829
$test
# A tibble: 8 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 MAE 2.46 NA NA
2 MSE 14.3 NA NA
3 RMSE 3.79 NA NA
4 rsq 0.839 NA NA
5 caret_rsq 0.838 NA NA
6 pearson 0.916 NA NA
7 spearman 0.903 NA NA
8 kendall 0.756 NA NA > binary_stats
$train
# A tibble: 18 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 c_index 0.995 NA NA
2 log_loss 0.0244 NA NA
3 AUC 1.00 NA NA
4 prAUC 0.990 NA NA
5 correct_rate 0.996 NA NA
6 kappa 0.990 NA NA
7 F1 0.994 NA NA
8 Se 0.994 NA NA
9 Sp 0.997 NA NA
10 PPV 0.994 NA NA
11 NPV 0.997 NA NA
12 precision 0.994 NA NA
13 recall 0.994 NA NA
14 detection_rate 0.349 NA NA
15 balanced_accuracy 0.995 NA NA
16 brier_score 0.00527 NA NA
17 class_p_outcome 0.983 NA NA
18 class_p_fitted 0.983 NA NA
$cv
# A tibble: 18 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <dbl> <dbl>
1 c_index 0.968 0.926 1
2 log_loss 0.128 0.0242 0.306
3 AUC 0.990 0.973 1
4 prAUC 0.929 0.782 0.975
5 correct_rate 0.969 0.930 1
6 kappa 0.933 0.846 1
7 F1 0.957 0.901 1
8 Se 0.962 0.913 1
9 Sp 0.973 0.932 1
10 PPV 0.952 0.884 1
11 NPV 0.980 0.952 1
12 precision 0.952 0.884 1
13 recall 0.962 0.913 1
14 detection_rate 0.338 0.321 0.352
15 balanced_accuracy 0.968 0.926 1
16 brier_score 0.0284 0.00516 0.0654
17 class_p_outcome 0.983 0.970 0.990
18 class_p_fitted 0.982 0.968 0.991
$test
# A tibble: 18 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 c_index 0.955 NA NA
2 log_loss 0.0972 NA NA
3 AUC 0.995 NA NA
4 prAUC 0.984 NA NA
5 correct_rate 0.960 NA NA
6 kappa 0.912 NA NA
7 F1 0.943 NA NA
8 Se 0.937 NA NA
9 Sp 0.973 NA NA
10 PPV 0.949 NA NA
11 NPV 0.966 NA NA
12 precision 0.949 NA NA
13 recall 0.937 NA NA
14 detection_rate 0.326 NA NA
15 balanced_accuracy 0.955 NA NA
16 brier_score 0.0300 NA NA
17 class_p_outcome 0.979 NA NA
18 class_p_fitted 0.978 NA NA> multi_class_stats
$train
# A tibble: 17 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 log_loss 0.000368 NA NA
2 AUC 1 NA NA
3 prAUC 0.974 NA NA
4 correct_rate 1 NA NA
5 kappa 1 NA NA
6 F1 1 NA NA
7 Se 1 NA NA
8 Sp 1 NA NA
9 PPV 1 NA NA
10 NPV 1 NA NA
11 precision 1 NA NA
12 recall 1 NA NA
13 detection_rate 0.333 NA NA
14 balanced_accuracy 1 NA NA
15 brier_score 0.00000195 NA NA
16 class_p_outcome 1.00 NA NA
17 class_p_fitted 1.00 NA NA
$cv
# A tibble: 17 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <dbl> <dbl>
1 log_loss 0.145 0.0103 0.533
2 AUC 0.998 0.987 1
3 prAUC 0.868 0.856 0.877
4 correct_rate 0.968 0.915 1
5 kappa 0.952 0.871 1
6 F1 0.969 0.919 1
7 Se 0.971 0.925 1
8 Sp 0.984 0.957 1
9 PPV 0.970 0.917 1
10 NPV 0.984 0.955 1
11 precision 0.970 0.917 1
12 recall 0.971 0.925 1
13 detection_rate 0.323 0.305 0.333
14 balanced_accuracy 0.978 0.941 1
15 brier_score 0.0596 0.00241 0.164
16 class_p_outcome 0.975 0.945 0.995
17 class_p_fitted 0.975 0.943 0.994
$test
# A tibble: 17 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <lgl> <lgl>
1 log_loss 0.00710 NA NA
2 AUC 1 NA NA
3 prAUC 0.947 NA NA
4 correct_rate 1 NA NA
5 kappa 1 NA NA
6 F1 1 NA NA
7 Se 1 NA NA
8 Sp 1 NA NA
9 PPV 1 NA NA
10 NPV 1 NA NA
11 precision 1 NA NA
12 recall 1 NA NA
13 detection_rate 0.333 NA NA
14 balanced_accuracy 1 NA NA
15 brier_score 0.00179 NA NA
16 class_p_outcome 0.992 NA NA
17 class_p_fitted 0.992 NA NA There are cases, when you would like to have a more detailed look at predictions of a machine learnig model in a particular suset of subsets of the data. This can be conveniently done by split() applied to a caretx model. The splitting factor - a categorical variable present in the training and, optionally, test data set - is speficied by the f argument. The split() method returns a plain list of prediction objects, which can be plotted or evaluated with plot() and summary() as described above. In this particular example, we would like to know, how the Boston house price model wors for objects located at and beyond the Charles River bank (coded by the chas variable in the Boston data set):
regression_chas <- split(my_models$regression,
f = chas,
newdata = my_boston$test)
regression_chas_stats <- regression_chas %>%
map(summary) regression_chas_stats[c("cv.no", "cv.yes")] %>%
map(filter, statistic == 'MAE')
$cv.no
# A tibble: 1 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <dbl> <dbl>
1 MAE 2.46 2.00 3.06
$cv.yes
# A tibble: 1 × 4
statistic estimate lower_ci upper_ci
<chr> <dbl> <dbl> <dbl>
1 MAE 2.18 0.897 4.96> clstats(my_models$multi_class, newdata = my_wines$test)
$train
# A tibble: 3 × 14
.outcome correct_rate kappa F1 Se Sp PPV NPV precision recall detection_rate balanced_accuracy brier_score class_p
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Barbera 1 1 1 1 1 1 1 1 1 0.269 1 0.0000000177 1.00
2 Barolo 1 1 1 1 1 1 1 1 1 0.328 1 0.0000000177 1.00
3 Grignolino 1 1 1 1 1 1 1 1 1 0.403 1 0.00000772 1.00
$cv
# A tibble: 3 × 14
.outcome correct_rate kappa F1 Se Sp PPV NPV precision recall detection_rate balanced_accuracy brier_score class_p
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Barbera 0.988 0.970 0.979 1 0.984 0.958 1 0.958 1 0.269 0.992 0.000302 0.997
2 Barolo 0.997 0.992 0.995 0.995 0.998 0.995 0.998 0.995 0.995 0.326 0.996 0.0103 0.988
3 Grignolino 0.988 0.975 0.985 0.971 1 1 0.981 1 0.971 0.392 0.985 0.0501 0.977
$test
# A tibble: 3 × 14
.outcome correct_rate kappa F1 Se Sp PPV NPV precision recall detection_rate balanced_accuracy brier_score class_p
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Barbera 1 1 1 1 1 1 1 1 1 0.276 1 0.00531 0.986
2 Barolo 0.983 0.961 0.974 1 0.974 0.95 1 0.95 1 0.328 0.987 0.000272 0.997
3 Grignolino 0.983 0.964 0.978 0.957 1 1 0.972 1 0.957 0.379 0.978 0.0873 0.995
class_roc_plots <- clplots(my_models$multi_class,
newdata = my_wines$test) %>%
map2(., c('Wines: training', 'Wines: CV', 'Wines: test'),
~.x +
labs(title = .y) +
theme(legend.position = 'bottom'))
class_roc_plots$train +
class_roc_plots$cv +
class_roc_plots$test +
plot_layout(ncol = 2)
- Kuhn M. Building predictive models in R using the caret package. J Stat Softw (2008) 28:1–26. doi:10.18637/jss.v028.i05
- Friedman JH. Greedy function approximation: A gradient boosting machine. https://doi.org/101214/aos/1013203451 (2001) 29:1189–1232. doi:10.1214/AOS/1013203451
- Cohen J. A Coefficient of Agreement for Nominal Scales. Educ Psychol Meas (1960) 20:37–46. doi:10.1177/001316446002000104
- McHugh ML. Interrater reliability: the kappa statistic. Biochem Medica (2012) 22:276. doi:10.11613/bm.2012.031
- Brier GW. VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY. Mon Weather Rev (1950) 78:1–3. doi:10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2
- Goldstein-Greenwood J. A Brief on Brier Scores | UVA Library. (2021) Available at: https://library.virginia.edu/data/articles/a-brief-on-brier-scores [Accessed September 5, 2023]