library(tidyverse)## Warning: package 'tidyverse' was built under R version 4.2.3## Warning: package 'ggplot2' was built under R version 4.2.3## Warning: package 'tibble' was built under R version 4.2.3## Warning: package 'tidyr' was built under R version 4.2.3## Warning: package 'readr' was built under R version 4.2.3## Warning: package 'purrr' was built under R version 4.2.3## Warning: package 'dplyr' was built under R version 4.2.3## Warning: package 'stringr' was built under R version 4.2.3## Warning: package 'forcats' was built under R version 4.2.3## Warning: package 'lubridate' was built under R version 4.2.3## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsmembers <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-09-22/members.csv')## Rows: 76519 Columns: 21
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (10): expedition_id, member_id, peak_id, peak_name, season, sex, citizen...
## dbl (5): year, age, highpoint_metres, death_height_metres, injury_height_me...
## lgl (6): hired, success, solo, oxygen_used, died, injured
##
## 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.members %>% glimpse()## Rows: 76,519
## Columns: 21
## $ expedition_id <chr> "AMAD78301", "AMAD78301", "AMAD78301", "AMAD78301…
## $ member_id <chr> "AMAD78301-01", "AMAD78301-02", "AMAD78301-03", "…
## $ peak_id <chr> "AMAD", "AMAD", "AMAD", "AMAD", "AMAD", "AMAD", "…
## $ peak_name <chr> "Ama Dablam", "Ama Dablam", "Ama Dablam", "Ama Da…
## $ year <dbl> 1978, 1978, 1978, 1978, 1978, 1978, 1978, 1978, 1…
## $ season <chr> "Autumn", "Autumn", "Autumn", "Autumn", "Autumn",…
## $ sex <chr> "M", "M", "M", "M", "M", "M", "M", "M", "M", "M",…
## $ age <dbl> 40, 41, 27, 40, 34, 25, 41, 29, 35, 37, 23, 44, 2…
## $ citizenship <chr> "France", "France", "France", "France", "France",…
## $ expedition_role <chr> "Leader", "Deputy Leader", "Climber", "Exp Doctor…
## $ hired <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ highpoint_metres <dbl> NA, 6000, NA, 6000, NA, 6000, 6000, 6000, NA, 681…
## $ success <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ solo <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ oxygen_used <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ died <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ death_cause <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ death_height_metres <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ injured <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, …
## $ injury_type <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
## $ injury_height_metres <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…Rows: 76,519 Columns: 21 $ expedition_id
Write code for a classification model that tells the probability of a member dying.
# Load libraries
library(tidymodels)## Warning: package 'tidymodels' was built under R version 4.2.3## ── Attaching packages ────────────────────────────────────── tidymodels 1.1.1 ──## ✔ broom 1.0.5 ✔ rsample 1.2.0
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## ✔ parsnip 1.1.1 ✔ yardstick 1.3.0
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## • Learn how to get started at https://www.tidymodels.org/start/library(h2o)## Warning: package 'h2o' was built under R version 4.2.3##
## ----------------------------------------------------------------------
##
## Your next step is to start H2O:
## > h2o.init()
##
## For H2O package documentation, ask for help:
## > ??h2o
##
## After starting H2O, you can use the Web UI at http://localhost:54321
## For more information visit https://docs.h2o.ai
##
## ----------------------------------------------------------------------##
## Attaching package: 'h2o'## The following objects are masked from 'package:lubridate':
##
## day, hour, month, week, year## The following objects are masked from 'package:stats':
##
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## colnames<-, ifelse, is.character, is.factor, is.numeric, log,
## log10, log1p, log2, round, signif, trunc# Load data
members_data <- members
# Preprocess data
members_data_processed <- members_data %>%
# Remove unnecessary columns
select(-expedition_id, -member_id, -peak_id, -peak_name) %>%
# Convert success to factor
mutate(success = as.factor(success),
# Convert solo to factor
solo = as.factor(solo),
# Convert oxygen_used to factor
oxygen_used = as.factor(oxygen_used),
# Convert died to factor
died = as.factor(died),
# Convert injured to factor
injured = as.factor(injured))
# Set seed
set.seed(456)
# Split data into training and testing sets
data_split <- initial_split(members_data_processed, prop = 0.7, strata = died)
train_data <- training(data_split)
test_data <- testing(data_split)
# Initialize h2o
h2o.init()## Connection successful!
##
## R is connected to the H2O cluster:
## H2O cluster uptime: 6 minutes 13 seconds
## H2O cluster timezone: America/New_York
## H2O data parsing timezone: UTC
## H2O cluster version: 3.44.0.3
## H2O cluster version age: 4 months and 11 days
## H2O cluster name: H2O_started_from_R_aesim_yfi818
## H2O cluster total nodes: 1
## H2O cluster total memory: 3.88 GB
## H2O cluster total cores: 8
## H2O cluster allowed cores: 8
## H2O cluster healthy: TRUE
## H2O Connection ip: localhost
## H2O Connection port: 54321
## H2O Connection proxy: NA
## H2O Internal Security: FALSE
## R Version: R version 4.2.2 (2022-10-31 ucrt)## Warning in h2o.clusterInfo():
## Your H2O cluster version is (4 months and 11 days) old. There may be a newer version available.
## Please download and install the latest version from: https://h2o-release.s3.amazonaws.com/h2o/latest_stable.html# Convert to h2o
train_h2o <- as.h2o(train_data)##
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|======================================================================| 100%test_h2o <- as.h2o(test_data)##
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|======================================================================| 100%# Define predictors and response variable
predictors <- setdiff(colnames(train_h2o), c("died"))
response <- "died"
# Train AutoML model
aml <- h2o.automl(x = predictors,
y = response,
training_frame = train_h2o,
leaderboard_frame = test_h2o,
max_runtime_secs = 30,
seed = 456)##
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|=== | 4%
## 14:02:08.240: AutoML: XGBoost is not available; skipping it.
## 14:02:08.244: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
## 14:02:09.441: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:13.140: _train param, Dropping unused columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:14.954: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:16.740: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:19.607: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:22.86: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:24.583: _train param, Dropping unused columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
## 14:02:26.627: _train param, Dropping unused columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:29.326: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:33.709: _train param, Dropping bad and constant columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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## 14:02:35.989: _train param, Dropping unused columns: [expedition_role, season, death_cause, sex, citizenship, injury_type]
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|======================================================================| 100%# View AutoML leaderboard
print(aml@leaderboard)## model_id auc logloss
## 1 StackedEnsemble_BestOfFamily_3_AutoML_2_20240502_140208 0.9958623 0.004196131
## 2 StackedEnsemble_BestOfFamily_1_AutoML_2_20240502_140208 0.9953959 0.004351004
## 3 GBM_1_AutoML_2_20240502_140208 0.9953568 0.004570709
## 4 GBM_4_AutoML_2_20240502_140208 0.9952180 0.005266805
## 5 StackedEnsemble_BestOfFamily_2_AutoML_2_20240502_140208 0.9948895 0.004304416
## 6 GBM_3_AutoML_2_20240502_140208 0.9948147 0.005240974
## aucpr mean_per_class_error rmse mse
## 1 0.9740063 0.01960041 0.02432935 0.0005919173
## 2 0.9722955 0.01960041 0.02442641 0.0005966497
## 3 0.9722806 0.01960041 0.02438116 0.0005944409
## 4 0.9726629 0.01960041 0.02479681 0.0006148817
## 5 0.9733986 0.01960041 0.02440678 0.0005956909
## 6 0.9734421 0.01960041 0.02496449 0.0006232257
##
## [13 rows x 7 columns]# Get best model from AutoML
best_model <- aml@leader
# Make predictions on test data
predictions <- h2o.predict(best_model, test_h2o)##
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|======================================================================| 100%# View predictions
head(predictions)## predict FALSE TRUE
## 1 FALSE 0.9997430 0.0002569530
## 2 FALSE 0.9997925 0.0002074977
## 3 FALSE 0.9995665 0.0004334634
## 4 FALSE 0.9998757 0.0001243475
## 5 FALSE 0.9997434 0.0002566410
## 6 FALSE 0.9998738 0.0001261594