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Course
- Data
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 recently worked at Facebook and is currently at United Technologies. He has degrees in engineering, business, and statistics. Chetan's main areas of expertise are in machine learning and statistical methods, and he is fluent in both Python and R. Chetan is eager to share his knowledge with budding data scientists on Pluralsight!
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