Implications of a Modeling Pipeline-2

The answer applies when using a threshold moving or probability calibration step at the end of the pipeline.

The reason is the same reason that we are not concerned about the specific internal structure or coefficients of the chosen model.

For example, when evaluating a logistic regression model, we don’t need to inspect the coefficients chosen on each k-fold cross-validation round in order to choose the model. Instead, we focus on its out-of-fold predictive skill

Similarly, when using a logistic regression model as the final model for making predictions on new data, we do not need to inspect the coefficients chosen when fitting the model on the entire dataset before making predictions.

We can inspect and discover the coefficients used by the model as an exercise in analysis, but it does not impact the selection and use of the model.

This same answer generalizes when considering a modeling pipeline.

We are not concerned about which features may have been automatically selected by a data transform in the pipeline. We are also not concerned about which hyperparameters were chosen for the model when using a grid search as the final step in the modeling pipeline.

In all three cases: the single model, the pipeline with automatic feature selection, and the pipeline with a grid search, we are evaluating the “model” or “modeling pipeline” as an atomic unit.

The pipeline allows us as machine learning practitioners to move up one level of abstraction and be less concerned with the specific outcomes of the algorithms and more concerned with the capability of a sequence of procedures.

As such, we can focus on evaluating the capability of the algorithms on the dataset, not the product of the algorithms, i.e. the model. Once we have an estimate of the pipeline, we can apply it and be confident that we will get similar performance, on average.

It is a shift in thinking and may take some time to get used to.

It is also the philosophy behind modern AutoML (automatic machine learning) techniques that treat applied machine learning as a large combinatorial search problem.