Cross validated, parameter tuned classifiers using sklearn

Once you have mastered some of the key modeling techniques for supervised learning, you might begin to hold a preference for a select few. However, regardless of your preference, a good data scientist understands there isn’t necessarily one perfect tool for every problem. It is the data scientist’s job to select the best model to make sense of the madness.

This is where the beauty of pipelines comes in.

Pipelines offer a streamlined technique for finding the best performance parameters for a model fit to…