8 Special Tasks

This chapter explores the different functions of mlr3 when dealing with specific data sets that require further statistical modification to undertake sensible analysis. Following topics are discussed:

Survival Analysis

Survival analysis is used to monitor the period of time until a specific event takes places. This specific event could be e.g. death, transmission of a disease, marriage or divorce.

Two considerations are important:

  • Whether the event occurred within the frame of the given data
  • How much time it took until the event occurred

In summary, this sub-chapter explains how to conduct survival analysis in mlr3.

Spatial Analysis

Spatial analysis data observations entail reference information about spatial characteristics. One of the largest shortcomings of spatial data analysis is the inevitable auto-correlation in spatial data. Auto-correlation is especially severe in data with marginal spatial variation.

This sub-chapter provides instructions on how to handle the problems associated with spatial data accordingly.

Ordinal Analysis

Coming soon!

Functional Analysis

Functional analysis contains data that consists of curves varying over a continuum e.g. time, frequency or wavelength. This type of analysis is frequently used when examining measurements over a period of time.

Steps on how to accommodate functional data structures in mlr3 are explained in the functional analysis-chapter.

Multilabel Classification

Multilabel classification deals with objects that can belong to more than one category at the same time. Numerous target labels are attributed to a single observation. Working with multilabel data requires one to use modified algorithms, to accommodate data specific characteristics. Two approaches to multilabel classification exist, namely the problem transformation method and the algorithm adaption method.

Instructions on how to deal with multilabel analysis in mlr3 can be found in this sub-chapter.

Cost Sensitive Classification

This sub-chapter deals with the implementation of cost-sensitive classification. Regular classification aims to minimize the misclassification rate and thus all types of misclassification errors are deemed equally severe. Cost-sensitive classification is a setting where the costs caused by different kinds of errors are not assumed to be equal and the objective is to minimize the expected costs.

This sub-chapter provides guidance on how to implement a first model. Subsequently, the sub-chapter contains instructions on how to modify cost sensitivity measures, thresholding and threshold tuning.