Python for Finance Cookbook

Implementing scikit-learn's pipelines

In the previous recipes, we showed all the steps required to build a machine learning model —starting with loading data, splitting it into a training and a test set, imputing missing values, encoding categorical features, and—lastly—fitting a decision tree classifier.

The process requires multiple steps to be executed in a certain order, which can sometimes be tricky with a lot of modifications to the pipeline mid-work. That is why scikit-learn introduced Pipelines. By using Pipelines, we can sequentially apply a list of transformations to the data, and then train a given estimator (model).

One important point to be aware of is that the intermediate steps of the Pipeline must have the fit and transform ...