Modeling Pipeline Optimization With scikit-learn
This tutorial presents two essential concepts in data science and automated learning. One is the machine learning pipeline, and the second is its optimization. These two principles are the key to implementing any successful intelligent system based on machine learning.
A machine learning pipeline can be created by putting together a sequence of steps involved in training a machine learning model. It can be used to automate a machine learning workflow. The pipeline can involve pre-processing, feature selection, classification/regression, and post-processing. More complex applications may need to fit in other necessary steps within this pipeline.
By optimization, we mean tuning the model for the best performance. The success of any learning model rests on the selection of the best