Chapter 6. Learning Best Practices for Model Evaluation and Hyperparameter Tuning
In the previous chapters, you learned about the essential machine learning algorithms for classification and how to get our data into shape before we feed it into those algorithms. Now, it's time to learn about the best practices of building good machine learning models by fine-tuning the algorithms and evaluating the model's performance! In this chapter, we will learn how to:
- Obtain unbiased estimates of a model's performance
- Diagnose the common problems of machine learning algorithms
- Fine-tune machine learning models
- Evaluate predictive models using different performance metrics
Streamlining workflows with pipelines
When we applied different preprocessing techniques in the previous chapters, such as standardization for feature scaling in Chapter 4, Building Good Training Sets – Data Preprocessing, or principal component analysis for data compression in Chapter 5, Compressing Data...
