Model Evaluation and Selection Using scikit-learn
Review the techniques and metrics used to evaluate how well your machine learning model performs. You will also learn methods to select the best machine learning model from a set of models that you've built.
What you'll learn
During the machine learning model building process, you will have to make some important decisions on how to evaluate how well your models perform, as well as how to select the best performing model. In this course, Model Evaluation and Selection Using scikit-learn, you will learn foundational knowledge/gain the ability to evaluate and select the best models. First, you will learn about a variety of metrics that you can use to evaluate how well your models are performing. Next, you will discover techniques for selecting the model that will perform the best in the future. Finally, you will explore how to implement this knowledge in Python, using the scikit-learn library. When you're finished with this course, you will have the skills and knowledge of needed to evaluate and select the best machine learning model from a set of models that you've built.
Table of contents
About the author
Chetan is an accomplished data scientist who has worked across a variety of industries, including financial services, retail, advertising, and manufacturing. Most recently, he worked as a data scientist at Facebook, and is currently a Director of Data Science at United Technologies, where he is in a leadership role. Chetan has an undergraduate degree in engineering from Cooper Union, an MBA from Yale University, and a Masters degree in Statistics from Baruch College. He is a life-long learner, a... more