Composite Pipelines
Composite Pipelines groups the core ideas a learner needs at the 5 · data preprocessing & feature engineering stage. Work through the lessons in order when new to the area, or use them independently as a reference when implementing an analysis.
Learn the mechanism one decision at a time
Work through the lessons in order if the topic is new. If you already know the basics, open the specific leaf lesson that matches the operation, diagnostic or failure mode you need.
1Definition→
2Mechanism→
3Example→
4Diagnostic→
5Decision
Pipeline conceptPipeline concept changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
02ColumnTransformer`ColumnTransformer` applies different preprocessing pipelines to different subsets of columns and concatenates the resulting feature matrix. It is one of the safest ways to keep numeric, categorical and other transformations inside the fitted ML pipeline.
03Different transforms by column typeDifferent transforms by column type changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
04Custom transformersCustom transformers changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
05Transformed targetsTransformed targets changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
06Pipeline parameter searchPipeline parameter search changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.
07Persisting the fitted pipelinePersisting the fitted pipeline changes how raw variables are represented for analysis or modelling. The transformation should preserve the information needed by the task while making assumptions explicit and reproducible.