Evaluating Model Performance

Assessing the performance of a machine learning model is a critical step in ensuring its effectiveness and reliability. Throughout this chapter, you will gain insights into the techniques and metrics used to evaluate model performance. Understanding these concepts will enable you to discern not just how accurate a model's predictions are, but also how well it generalizes to unseen data.

You will begin by exploring various metrics commonly used to evaluate models, such as accuracy, precision, recall, and F1-score. Each of these metrics provides a different perspective on a model's performance, which is essential for classification tasks. For regression tasks, you will learn about metrics like mean squared error and mean absolute error.

The discussion will also cover the importance of splitting data into training and testing sets to avoid overfitting, a situation where a model performs well on training data but poorly on new, unseen data. By the end of this chapter, you will have a solid understanding of how to evaluate models effectively, setting the stage for selecting the appropriate model for your specific needs.