Synthetic Diabetes Classification Task

A synthetic binary classification task that mimics the structure of the former pima task. It has the same eight numeric features and a diabetes target with the positive class set to "pos". Some feature columns contain missing values, which makes the task useful for preprocessing examples and tests. The data is fully synthetic and contains no real patient data.

Format

R6::R6Class inheriting from TaskClassif.

Source

The data set is generated deterministically by the script in system.file("extdata", "diabetes.R", package = "mlr3").

Dictionary

This Task can be instantiated via the dictionary mlr_tasks or with the associated sugar function tsk():

mlr_tasks$get("diabetes")
tsk("diabetes")

Meta Information

• Task type: “classif”

• Dimensions: 128x9

• Properties: “twoclass”

• Has Missings: TRUE

• Target: “diabetes”

• Features: “age”, “glucose”, “insulin”, “mass”, “pedigree”, “pregnant”, “pressure”, “triceps”

See also

• Chapter in the mlr3book: https://mlr3book.mlr-org.com/chapters/chapter2/data_and_basic_modeling.html

• Package mlr3data for more toy tasks.

• Package mlr3oml for downloading tasks from https://www.openml.org.

• Package mlr3viz for some generic visualizations.

• Dictionary of Tasks: mlr_tasks

• as.data.table(mlr_tasks) for a table of available Tasks in the running session (depending on the loaded packages).

• mlr3fselect and mlr3filters for feature selection and feature filtering.

• Extension packages for additional task types:

  • Unsupervised clustering: mlr3cluster

  • Probabilistic supervised regression and survival analysis: https://mlr3proba.mlr-org.com/.

Other Task: Task, TaskClassif, TaskRegr, TaskSupervised, TaskUnsupervised, california_housing, mlr_tasks, mlr_tasks_breast_cancer, mlr_tasks_german_credit, mlr_tasks_iris, mlr_tasks_mtcars, mlr_tasks_penguins, mlr_tasks_sonar, mlr_tasks_spam, mlr_tasks_wine, mlr_tasks_zoo