1 Load packages
library(tidyverse) # data wrangling
library(tidymodels) # modelling2 Objective
Predicting the survival in the Titanic disaster. We’ll be using a tidymodels approach.
3 Load and prepare data
3.1 Hide details in a function
prepare_data <- function(traindata_url = "https://raw.githubusercontent.com/sebastiansauer/Statistiklehre/main/data/titanic/train.csv",
testdata_url = "https://raw.githubusercontent.com/sebastiansauer/Statistiklehre/main/data/titanic/test.csv") {
# import the data:
train <- read_csv(traindata_url)
test <- read_csv(testdata_url)
# bind both samples into one:
data_raw <-
train %>%
bind_rows(test)
# drop unused variables:
data <-
data_raw %>%
select(-c(Name, Cabin, Ticket))
# convert string to factors:
data2 <-
data %>%
mutate(across(where(is.character), as.factor))
# convert numeric outcome to nominal, to indicate classification:
data2 <- data2 %>%
mutate(Survived = as.factor(Survived))
return(data2)
}data2 <- prepare_data()4 Split data into train and test
split_titanic <- initial_time_split(data = data2, prop = 891/1309)
train <- training(split_titanic)
test <- testing(split_titanic)5 Define recipe
titanic_recipe <-
# define model formula:
recipe(Survived ~ ., data = train) %>%
# Use "ID" etc as ID, not as predictor:
update_role(PassengerId, new_role = "ID") %>%
# impute missing values:
step_knnimpute(all_predictors(), neighbors = 3) %>%
# convert character and factor type variables into dummy variables:
step_dummy(all_nominal(), -all_outcomes()) %>%
# exclude near zero variance predictors:
step_nzv(all_predictors()) %>%
# exclude highly correlated vars:
step_corr(all_predictors()) %>%
# center (set mean to zero):
step_center(all_predictors(), -all_outcomes()) %>%
# set sd=1
step_scale(all_predictors(), -all_outcomes()) 6 Define model
boost_mod <-
boost_tree(mtry = 100,
min_n = 10,
learn_rate = 0.1,
tree_depth = 4) %>%
set_engine("xgboost") %>%
set_mode("classification")See details of the model:
boost_mod %>%
translate()
#> Boosted Tree Model Specification (classification)
#>
#> Main Arguments:
#> mtry = 100
#> min_n = 10
#> tree_depth = 4
#> learn_rate = 0.1
#>
#> Computational engine: xgboost
#>
#> Model fit template:
#> parsnip::xgb_train(x = missing_arg(), y = missing_arg(), colsample_bytree = 100,
#> min_child_weight = 10, max_depth = 4, eta = 0.1, nthread = 1,
#> verbose = 0)7 Define workflow
titanic_boost_wf1 <-
workflow() %>%
add_model(boost_mod) %>%
add_recipe(titanic_recipe)8 Fit the model
titanic_boost_wf1 <-
titanic_boost_wf1 %>%
fit(data = train)9 Predict the test data
Test data need to be baked (and hence prepped):
test_baked <- bake(prep(titanic_recipe),
new_data = test)test_with_pred <-
test_baked %>%
bind_cols(predict(titanic_boost_wf1, test)) %>%
rename(pred_glm1 = .pred_class)10 Save csv file to disk
test_with_pred %>%
select(PassengerId, Survived = pred_glm1) %>%
write_csv(file = "titanic-boost1.csv")11 Reproducibility
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