Real-World Machine Learning

Chapter 4. Model evaluation and optimization

This chapter covers

• Using cross-validation for properly evaluating the predictive performance of models
• Overfitting and how to avoid it
• Standard evaluation metrics and visualizations for binary and multiclass classification
• Standard evaluation metrics and visualizations for regression models
• Optimizing your model by selecting the optimal parameters

After you fit a machine-learning model, the next step is to assess the accuracy of that model. Before you can put a model to use, you need to know how well it’s expected to predict on new data. If you determine that the predictive performance is quite good, you can be comfortable in deploying that model in production to analyze new data. Likewise, ...