Agile Machine Learning with DataRobot by Bipin Chadha & Sylvester Juwe

Author:Bipin Chadha & Sylvester Juwe [Bipin Chadha]
Language: eng
Format: epub
Publisher: Packt Publishing
Published: 2021-12-24T00:00:00+00:00

Chapter 7: Model Understanding and Explainability

In the last chapter, we learned how to build models, and we will now learn how to use output generated by DataRobot to understand the models and also use this information to explain why a model provides a particular prediction. As we have discussed before, this aspect is critically important to ensure that we are using the results correctly. DataRobot automates much of the task of creating charts and plots to help someone understand a model, but you still need to know how to interpret what it is showing in the context of the problem you are trying to solve. This is another reason why we will need people involved in the process, even if much of a task has been automated. As you can imagine, the task of interpreting the results will therefore become more and more valuable as the degree of automation increases.

In this chapter, we're going to cover the following main topics:

Reviewing and understanding model details

Assessing model performance and metrics

Generating model explanations

Understanding model learning curves and trade-offs

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