Apply 7
Adam Cilley
2025-04-01
Goal: Predict spam emails based on word frequency features
Import Data
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## You might also be interested in applied data science training for business.
## </> Learn more at - www.business-science.io </>library(tidymodels)## Warning: package 'tidymodels' was built under R version 4.4.2## ── Attaching packages ────────────────────────────────────── tidymodels 1.2.0 ──
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## • Search for functions across packages at https://www.tidymodels.org/find/library(themis)## Warning: package 'themis' was built under R version 4.4.3spam <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/main/data/2023/2023-08-15/spam.csv')## Rows: 4601 Columns: 7
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): yesno
## dbl (6): crl.tot, dollar, bang, money, n000, make
##
## 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.Clean Data
skimr::skim(spam)| Name | spam |
| Number of rows | 4601 |
| Number of columns | 7 |
| _______________________ | |
| Column type frequency: | |
| character | 1 |
| numeric | 6 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| yesno | 0 | 1 | 1 | 1 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| crl.tot | 0 | 1 | 283.29 | 606.35 | 1 | 35 | 95 | 266.00 | 15841.00 | ▇▁▁▁▁ |
| dollar | 0 | 1 | 0.08 | 0.25 | 0 | 0 | 0 | 0.05 | 6.00 | ▇▁▁▁▁ |
| bang | 0 | 1 | 0.27 | 0.82 | 0 | 0 | 0 | 0.32 | 32.48 | ▇▁▁▁▁ |
| money | 0 | 1 | 0.09 | 0.44 | 0 | 0 | 0 | 0.00 | 12.50 | ▇▁▁▁▁ |
| n000 | 0 | 1 | 0.10 | 0.35 | 0 | 0 | 0 | 0.00 | 5.45 | ▇▁▁▁▁ |
| make | 0 | 1 | 0.10 | 0.31 | 0 | 0 | 0 | 0.00 | 4.54 | ▇▁▁▁▁ |
Issues with Data:
- No missing values
- The target variable is
yesno(spam or not spam) - Numeric variables representing word frequencies
- No apparent ID variables to drop
- Need to convert the target variable into a binary format
spam_clean <- spam %>%
mutate(yesno = factor(yesno, levels = c("y", "n")))
spam_clean## # A tibble: 4,601 × 7
## crl.tot dollar bang money n000 make yesno
## <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
## 1 278 0 0.778 0 0 0 y
## 2 1028 0.18 0.372 0.43 0.43 0.21 y
## 3 2259 0.184 0.276 0.06 1.16 0.06 y
## 4 191 0 0.137 0 0 0 y
## 5 191 0 0.135 0 0 0 y
## 6 54 0 0 0 0 0 y
## 7 112 0.054 0.164 0 0 0 y
## 8 49 0 0 0 0 0 y
## 9 1257 0.203 0.181 0.15 0 0.15 y
## 10 749 0.081 0.244 0 0.19 0.06 y
## # ℹ 4,591 more rowsExplore Data
spam_clean %>% count(yesno)## # A tibble: 2 × 2
## yesno n
## <fct> <int>
## 1 y 1813
## 2 n 2788spam_clean %>%
ggplot(aes(yesno)) +
geom_bar()
Spam vs. CRL Total
spam_clean %>%
ggplot(aes(yesno, crl.tot)) +
geom_boxplot()
Correlation Analysis
# Step 1: Binarize
spam_binarized <- spam_clean %>%
binarize()
spam_binarized %>% glimpse()## Rows: 4,601
## Columns: 17
## $ `crl.tot__-Inf_35` <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0…
## $ crl.tot__35_95 <dbl> 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ crl.tot__95_266 <dbl> 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1…
## $ crl.tot__266_Inf <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
## $ `dollar__-Inf_0.052` <dbl> 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1…
## $ dollar__0.052_Inf <dbl> 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0…
## $ `bang__-Inf_0.315` <dbl> 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0…
## $ bang__0.315_Inf <dbl> 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 1…
## $ money__0 <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1…
## $ `money__-OTHER` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0…
## $ n000__0 <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1…
## $ `n000__-OTHER` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0…
## $ make__0 <dbl> 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1…
## $ make__0.1 <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…
## $ `make__-OTHER` <dbl> 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0…
## $ yesno__y <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
## $ yesno__n <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0…# Step 2: Correlation
spam_correlation <- spam_binarized %>%
correlate(yesno__y)
# Step 3: Sort and Analyze Correlations
spam_correlation_sorted <- spam_correlation %>%
arrange(desc(correlation))
spam_correlation_sorted## # A tibble: 17 × 3
## feature bin correlation
## <fct> <chr> <dbl>
## 1 yesno y 1
## 2 dollar 0.052_Inf 0.566
## 3 bang 0.315_Inf 0.490
## 4 money -OTHER 0.475
## 5 n000 -OTHER 0.419
## 6 crl.tot 266_Inf 0.299
## 7 make -OTHER 0.223
## 8 crl.tot 95_266 0.145
## 9 make 0.1 0.0803
## 10 crl.tot 35_95 -0.0818
## 11 make 0 -0.239
## 12 crl.tot -Inf_35 -0.360
## 13 n000 0 -0.419
## 14 money 0 -0.475
## 15 bang -Inf_0.315 -0.490
## 16 dollar -Inf_0.052 -0.566
## 17 yesno n -1# Step 4: Plot
spam_correlation_sorted %>%
correlationfunnel::plot_correlation_funnel()
Split Data
set.seed(1234)
spam_split <- initial_split(spam_clean, strata = yesno)
spam_train <- training(spam_split)
spam_test <- testing(spam_split)
spam_cv <- vfold_cv(spam_train, strata = yesno)
spam_cv## # 10-fold cross-validation using stratification
## # A tibble: 10 × 2
## splits id
## <list> <chr>
## 1 <split [3104/346]> Fold01
## 2 <split [3105/345]> Fold02
## 3 <split [3105/345]> Fold03
## 4 <split [3105/345]> Fold04
## 5 <split [3105/345]> Fold05
## 6 <split [3105/345]> Fold06
## 7 <split [3105/345]> Fold07
## 8 <split [3105/345]> Fold08
## 9 <split [3105/345]> Fold09
## 10 <split [3106/344]> Fold10Preprocess Data
spam_recipe <- recipe(yesno ~ ., data = spam_train) %>%
step_YeoJohnson(all_numeric_predictors())
spam_recipe %>% prep() %>% juice() %>% glimpse()## Rows: 3,450
## Columns: 7
## $ crl.tot <dbl> 3.260223, 4.132217, 7.347801, 3.541443, 4.935058, 3.330126, 2.…
## $ dollar <dbl> 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000, 0.000,…
## $ bang <dbl> 0.10652716, 0.15135901, 0.00000000, 0.00000000, 0.00000000, 0.…
## $ money <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.…
## $ n000 <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.…
## $ make <dbl> 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.…
## $ yesno <fct> n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n, n,…Specify Model
spam_xgboost_spec <-
boost_tree(trees = tune()) %>%
set_mode("classification") %>%
set_engine("xgboost")
spam_xgboost_workflow <-
workflow() %>%
add_recipe(spam_recipe) %>%
add_model(spam_xgboost_spec) Tune Hyperparameters
doParallel::registerDoParallel()
set.seed(43931)
spam_xgboost_tune <-
tune_grid(spam_xgboost_workflow,
resamples = spam_cv,
grid = 5,
control = control_grid(save_pred = TRUE))Model Evaluation
Identify optimal values for hyperparameters
collect_metrics(spam_xgboost_tune)## # A tibble: 15 × 7
## trees .metric .estimator mean n std_err .config
## <int> <chr> <chr> <dbl> <int> <dbl> <chr>
## 1 356 accuracy binary 0.868 10 0.00429 Preprocessor1_Model1
## 2 356 brier_class binary 0.106 10 0.00317 Preprocessor1_Model1
## 3 356 roc_auc binary 0.909 10 0.00334 Preprocessor1_Model1
## 4 425 accuracy binary 0.868 10 0.00352 Preprocessor1_Model2
## 5 425 brier_class binary 0.107 10 0.00312 Preprocessor1_Model2
## 6 425 roc_auc binary 0.907 10 0.00341 Preprocessor1_Model2
## 7 909 accuracy binary 0.865 10 0.00430 Preprocessor1_Model3
## 8 909 brier_class binary 0.113 10 0.00311 Preprocessor1_Model3
## 9 909 roc_auc binary 0.901 10 0.00320 Preprocessor1_Model3
## 10 1505 accuracy binary 0.863 10 0.00436 Preprocessor1_Model4
## 11 1505 brier_class binary 0.117 10 0.00321 Preprocessor1_Model4
## 12 1505 roc_auc binary 0.899 10 0.00342 Preprocessor1_Model4
## 13 1733 accuracy binary 0.863 10 0.00456 Preprocessor1_Model5
## 14 1733 brier_class binary 0.118 10 0.00322 Preprocessor1_Model5
## 15 1733 roc_auc binary 0.898 10 0.00341 Preprocessor1_Model5collect_predictions(spam_xgboost_tune) %>%
group_by(id) %>%
roc_curve(yesno, .pred_y) %>%
autoplot()
Fit the model for the last time
spam_xgboost_last <- spam_xgboost_workflow %>%
finalize_workflow(select_best(spam_xgboost_tune, metric = "accuracy")) %>%
last_fit(spam_split)## Warning: package 'xgboost' was built under R version 4.4.2collect_metrics(spam_xgboost_last)## # A tibble: 3 × 4
## .metric .estimator .estimate .config
## <chr> <chr> <dbl> <chr>
## 1 accuracy binary 0.867 Preprocessor1_Model1
## 2 roc_auc binary 0.913 Preprocessor1_Model1
## 3 brier_class binary 0.102 Preprocessor1_Model1collect_predictions(spam_xgboost_last) %>%
yardstick::conf_mat(yesno, .pred_class) %>%
autoplot()
Variable Importance
library(vip)## Warning: package 'vip' was built under R version 4.4.3##
## Attaching package: 'vip'## The following object is masked from 'package:utils':
##
## vispam_xgboost_last %>%
workflows::extract_fit_engine() %>%
vip()