Pipelines
As experiments grow, so does the complexity of the operations. We may split up our dataset, binarize features, perform feature-based scaling, perform sample-based scaling, and many more operations.
Keeping track of all of these operations can get quite confusing and can result in being unable to replicate the result. Problems include forgetting a step, incorrectly applying a transformation, or adding a transformation that wasn't needed.
Another issue is the order of the code. In the previous section, we created our X_transformed dataset and then created a new estimator for the cross validation. If we had multiple steps, we would need to track all of these changes to the dataset in the code.
Pipelines are a construct that addresses these problems (and others, which we will see in the next chapter). Pipelines store the steps in your data mining workflow. They can take your raw data in, perform all the necessary transformations, and then create a prediction. This allows us to use...
