Sequential Pipelines
MCML Authors
Abstract
Abstract
Computational pipelines provide a layer of abstraction for swapping in and out different elements of the pipeline. In machine learning this can be useful for swapping algorithms, as well as common operations for data preprocessing and model post processing. Many real-world machine learning applications involve more than just fitting a single model at a time: It is often beneficial or even necessary to preprocess data for feature engineering and compatibility with learners. In many cases it is also useful to combine predictions of multiple models in ensembles. By defining these workflows as computational objects, it is then possible to treat them like models to be trained/tested and even tuned. This chapter introduces mlr3pipelines, a dataflow programming language that can be used to define machine learning processes from simple building blocks. The chapter focuses on sequential pipelines, in which data passes from one operation to another in a linear sequence and each operation has one input and output. The chapter introduces PipeOp and Graph, which are the building blocks of a pipeline, and provides some concrete examples with PCA.
article
Applied Machine Learning Using mlr3 in R
II.7. Jan. 2024.Authors
M. Binder • F. PfistererLinks
DOIResearch Area
BibTeXKey: BP24a