Streamlining workflows with pipelines

When we applied different preprocessing techniques in the previous chapters, such as standardization for feature scaling in Chapter 4, Building Good Training Sets – Data Preprocessing, or principal component analysis for data compression in Chapter 5, Compressing Data via Dimensionality Reduction, you learned that we have to reuse the parameters that were obtained during the fitting of the training data to scale and compress any new data, for example, the samples in the separate test dataset. In this section, you will learn about an extremely handy tool, the Pipeline class in scikit-learn. It allows us to fit a model including an arbitrary number of transformation steps and apply it to make predictions about new data.

Loading the Breast Cancer Wisconsin dataset

In this chapter, we will be working with the Breast Cancer Wisconsin dataset, which contains 569 samples of malignant and benign tumor cells. The first two columns in the dataset store the unique...