4
Detecting Performance and Efficiency Issues in Machine Learning Models
One of the main objectives we must keep in mind is how to build a high-performance machine learning model with minimal errors on new data we want to use the model for. In this chapter, you will learn how to properly assess the performance of your models and identify opportunities for decreasing their errors.
This chapter includes many figures and code examples to help you better understand these concepts and start benefiting from them in your projects.
We will cover the following topics:
- Performance and error assessment measures
- Visualization
- Bias and variance diagnosis
- Model validation strategy
- Error analysis
- Beyond performance
By the end of this chapter, you will have ...
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